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SEPAWA 2024

Posted on September 4, 2024September 30, 2024 by Anonymous
Conference

SEPAWA 2024

CalendarDate & Time
  • October 16th-18th, 2024
LocationLocation
  • Berlin, Germany
Register

Schrödinger is excited to be participating in the SEPAWA 2024 conference taking place on October 16th – 18th in Berlin, Germany. Join us for a presentation by Jeff Sanders, Senior Principal Scientist at Schrödinger, titled “Beyond AI: The importance of Physics-based Modeling and Machine Learning to Develop New Cosmetic Products.”

icon time OCT 18 | 10:45 – 11:15
icon location Room 15
Beyond AI: The importance of Physics-based Modeling and Machine Learning to Develop New Cosmetic Products

Speaker:
Jeff Sanders, Senior Principal Scientist

Abstract:
Despite the increasing popularity of machine learning and AI tools in consumer goods industries, methods are often applied ad hoc, lacking systematic data sampling or a comprehensive understanding of resulting ML models. This can lead to a knowledge gap between ML/AI proponents and researchers, especially when datasets are small or not easily transferable. Newer methodologies, like active learning, aim to bridge this gap by integrating physics-based simulation and experimental data to not only construct models but also identify blind spots in relevant property space coverage. In cosmetic formulation research projects where chemistry and composition are crucial, active learning can be utilized to develop models and guide experimentation in an iterative feedback loop, offering unique insights into model performance. By prioritizing chemistry first, many physicochemical descriptors can be generated using physics-based simulations, thereby enriching experimental datasets and averting the “blackboxing” of valuable models.

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The importance of human know-how in AI execution for R&D

Posted on August 28, 2024August 5, 2026 by Anonymous

The importance of human know-how in AI execution for R&D

How Schrödinger’s materials science domain experts ensure partner success

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The importance of human know-how in AI execution for R&D

Overview

As artificial intelligence (AI) and machine learning (ML) technologies rapidly advance, materials scientists, executives, and R&D professionals are being tasked with developing an AI/ML strategy to drive innovation. Yet while AI/ML tools may advertise the appeal of push-button innovation, this is almost never the reality. Even the term AI itself can be a misleading buzzword.

Schrödinger is uniquely positioned to partner with materials R&D teams to execute AI/ML strategies that deliver true business value because we leverage the proven accuracy of physics-based modeling, the speed and scale of machine learning, and the deep domain expertise of our materials scientists. So while AI/ML technologies can be transformative, gaining a competitive advantage with AI is only possible with the talent, vision, and know-how of people who can utilize and direct these technologies towards meaningful, impactful outcomes.

Personalized human support is critical for project efficiency and productivity

Schrödinger’s professional software support is unique in providing domain expertise, personalized assistance, and reliable service. This human difference ensures that users can fully leverage the potential of digital tools to address complex issues through tailored guidance and support.

Schrödinger Support Benefits
24-7 global support from a team of experts Faster project timelines
On-site and virtual assistance with expert application scientists Technical discussions, troubleshooting, and knowledge transfer
Extensive online training, tutorials, and resources Skilled users to maximize outcomes
Expert-led customized modeling service packages Novel state-of-the-art research, knowledge transfer and improved success rates

 

“As a former industrial modeler at Boeing, I understand that the needs of each client are unique. That’s why we focus on delivering personalized support that addresses their specific challenges in R&D digitization.”
Andrea Browning Director of Polymers and Soft Matter
Andrea Browning
Director of Polymers and Soft Matter

Dr. Andrea Browning is leading the efforts related to polymer and soft matter simulation at Schrödinger. Prior to joining Schrödinger in 2017, she was a lead research engineer and project manager at The Boeing Company. She brings over a decade of experience in connecting simulations to industrial decisions, and has helped industry innovators on challenging projects such as developing bio-based polymer formulations, optimizing polymer matrix in carbon fiber composites, and designing electrolyte molecules.

Schrödinger technical experts increase capacity and capability

Through strategic partnerships or customized contract research, Schrödinger’s team of expert scientists work closely with customers to tackle challenging problems by deploying digital chemistry strategies to guide rapid materials design and optimization. By working as a team, we share the same challenges and goals. These expert-driven collaborations transform challenges into opportunities, driving innovation and delivering exceptional results.

“Over the course of 20 years working with companies in this sector, I am convinced that collaboration is the key to delivering value, by listening to industry needs and tailoring the modeling solution.”
Simon Elliott Director of Atomic Level Process Simulation
Simon Elliott
Director of Atomic Level Process Simulation

Dr. Simon Elliott is a pioneer in applying atomic-scale models to the chemistry of thin film material deposition and etch. In this field he has chaired international conferences, coordinated transnational networks and authored about 100 peer-reviewed papers. He is recognized in the semiconductor industry for his work with chemical companies on design of precursor gases and with equipment suppliers on optimizing atomic layer deposition processes. He was the 2023 recipient of the ALD Innovator Award.

Expertise and industry-grade software can be the difference between project success and failure

It is common for materials science researchers to piece together a variety of modeling and simulation tools. Free software packages are often limited by outdated documentation, insufficient support, and obstacles to integration and automation. The human element is one of the key differentiators between industry-grade software like Schrödinger and less sophisticated software packages.

Schrödinger has a large team working on user experience, technical support, and education. This team ensures that clients can seamlessly access the science underpinning the simulations and use it at a scale that allows them to get their job done.

“Having used free software extensively in the past, I now realize how much time I was spending on troubleshooting rather than actual research. Schrödinger’s solutions are a game-changer for productivity.”
Pavel Dub Senior Principal Scientist and Product Manager
Pavel Dub
Senior Principal Scientist and Product Manager

Dr. Pavel Dub is an expert in Catalysis & Reactivity. With a robust foundation built through dual doctoral studies in Russia (2009) and France (2010), followed by postdoctoral research at the Tokyo Institute of Technology and Los Alamos National Laboratory, Pavel’s research has evolved from experimental organometallic chemistry and homogeneous catalysis to computational chemistry and materials science, utilizing both classical and quantum computing platforms. Before joining Schrödinger in 2022, Pavel served as a Staff Scientist at Los Alamos National Laboratory, where he led the development of quantum algorithms for solving complex chemical problems. He has worked with clients on advanced topics, including molecular catalyst design, automated reactivity screening, and reaction network generation.

Advanced physics-based modeling and AI/ML software for R&D teams

Schrödinger brings more than 30 years of scientific innovation and deep domain expertise to empower R&D teams to solve their unique challenges with computational approaches.

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Recent Testimonials

“By working closely with Schrödinger experts, we were impressed by how fast we were able to learn to apply molecular simulations, even with no prior modeling experience. Our collaborations have been very successful, not only because of our satisfaction with Schrödinger’s advanced technologies, but also because of their level of scientific expertise, support, and collaborative openness.”
Martin SettleSenior Research Manager, Reckitt
The resin design and incubation team at SABIC worked closely with Schrödinger’s material science team to build accurate machine learning (ML) models to speed up the discovery of new polymers. “These computational results are highly promising and can potentially shorten our polymer innovation timelines from traditionally a couple of years to only a couple of months.”
Vaidya RamakrishnanStaff Scientist, SABIC
“Schrödinger provides us with more than just software as part of our service agreement—they are a true partner in our research. With an office here in Japan, Schrödinger scientists and engineers are easily accessible and able to collaborate in-person with our team.”
Nobuyuki N. MatsuzawaExecutive Engineer, Panasonic Industry Co., Ltd.

Software and services to meet your organizational needs

Software Platform

Deploy digital materials discovery workflows with a comprehensive and user-friendly platform grounded in physics-based molecular modeling, machine learning, and team collaboration.

Research Services

Leverage Schrödinger’s expert computational scientists to assist at key stages in your materials discovery and development process.

Support & Training

Access expert support, educational materials, and training resources designed for both novice and experienced users.

Accelerated in silico discovery of SGR-1505: A potent MALT1 allosteric inhibitor for the treatment of mature B-cell malignancies

Posted on August 26, 2024March 13, 2025 by Anonymous
SEP 12, 2024

Accelerated in silico discovery of SGR-1505: A potent MALT1 allosteric inhibitor for the treatment of mature B-cell malignancies

Abstract:

MALT1 (Mucosa-associated lymphoid tissue lymphoma translocation protein 1) is a component of the MALT1-BCL10-CARD11 complex downstream from the Bruton Tyrosine Kinase (BTK) on the B-cell receptor signaling pathway. MALT1 is a key mediator of nuclear factor kappa B (NF-κB) signaling, which is the main driver of a subset of B-cell lymphomas. MALT1 is considered a potential therapeutic target for several subtypes of non-Hodgkin’s B-cell lymphomas and chronic lymphocytic leukemia (CLL), including tumors with acquired BTK inhibitor (BTKi) resistance. Constitutive activation of the NF-κB is a molecular hallmark of activated B cell-like diffuse large B cell lymphoma (ABC-DLBCL), and MALT1 may have utility as a treatment option for ABC-DLBCL. Furthermore, a third-party MALT1 inhibitor recently showed strong anti-tumor activity in mature B cell malignancies from Phase 1 studies.

By applying advanced physics-based modeling techniques, including combining free energy calculations with machine learning methods and chemistry-aware compound enumeration workflow, the Schrödinger team explored extensive sets of de novo design ideas to quickly identify a novel hit series with an in vivo tool molecule to establish an in vivo PD and efficacy mouse model early on in the project. Multi-parameter optimization (MPO) allowed efficient prioritization of molecules with good potency and drug-like properties during lead optimization. This led to the discovery of a highly potent MALT1 inhibitor, SGR-1505, with a well-balanced property profile in under a year, with only 78 compounds synthesized in the lead series and 129 compounds overall. SGR-1505 is a potent and orally available allosteric MALT1 inhibitor. It demonstrated strong anti-tumor activity alone and in combination with BTK inhibitors in multiple in vivo B-cell lymphoma xenograft models. Currently, a Phase 1 clinical trial with SGR-1505 in patients with mature B-cell neoplasms is ongoing (NCT05544019).

 

Webinar Highlights:

  • Discover how free energy calculations, amplified by machine learning methods, led to the discovery of a highly potent MALT1 inhibitor, SGR-1505
  • Learn how the team used an MPO scoring function consisting of FEP+-based predictions of affinity and solubility, physics-based predictions of permeability, and predictions of lipophilicity to optimize compounds
  • Ask questions to gain further insight from the speakers to apply to your work

Our Speakers

Goran Krilov

Senior Director, Schrödinger

Dr. Goran Krilov is a senior Director of Computational Chemistry at Schrödinger’s Therapeutic Group. For the past twenty five years, his work has focused on developing and applying cutting-edge computational chemistry techniques to problems in biophysics and drug discovery. He has led the modeling efforts on a number of internal projects as well as external collaborations in oncology and neurogenerative diseases, resulting in two clinical candidates currently undergoing Phase I trials. Prior to joining Schrödinger, Dr. Krilov has worked in both industry and academia, including IBM, Boston College snd Strand Life Sciences.

Zhe Nie

Executive Director, Schrödinger

Dr. Zhe Nie is the Executive Director of Medicinal Chemistry at Schrödinger’s Therapeutic Group. She has been leading multiple wholly owned and partnered drug discovery programs at Schrödinger. Most recently, she led Schrödinger’s MALT1 discovery project team, successfully developed the small molecule drug SGR-1505 (Schrödinger’s first internal clinical asset currently in Ph1) applying Schrödinger’s computational platform. It took less than two years from the start of the project to the selection of the clinical candidate. She also led the DLK collaboration project with Takeda Pharmaceuticals which discovered a potent, selective, and brain-penetrate DLK inhibitor as a promising preclinical candidate for the treatment of neurodegenerative diseases using Schrödinger’s computational platform. She has extensive experiences in applying advanced computational tools to assist in the design of small molecule drug candidates. She previously worked at Takeda, Celgene and Quanticel Pharmaceuticals (acquired by Celgene), led and contributed to advancing multiple small molecule drugs to the clinics including TAK-960, TAK-659 and CC-90011.

Webinar Series: From Molecules to Materials Applications

Posted on August 16, 2024January 22, 2025 by Anonymous
Webinar Series

From Molecules to Materials Applications

CalendarDate & Time
  • September 11th – October 8th, 2024
  • 18:00 IST
LocationLocation
  • Virtual
Register

Molecular modeling is a powerful computational technique widely used in materials science to predict and understand the properties and behavior of materials at the molecular level. By simulating the interactions between atoms and molecules, researchers can explore the structural, mechanical, electronic, and thermal properties of various materials and gain a deeper understanding. Molecular modeling encompasses a range of methods, including molecular dynamics, quantum mechanics, and coarse grained simulations, each providing unique insights into material properties and guiding experimental efforts.

The integration of molecular modeling into materials science can accelerate the development of advanced materials for applications in pharmaceuticals, Fast Moving Consumer Goods, electronics, energy storage, catalysis, and more.

This webinar series “From molecules to Materials Applications” will delve into molecular modeling techniques and their transformative impact on Materials Science research using the Schrödinger Materials Science tools.

icon time SEPT 11 | 18:00 IST
Molecular Modeling: A Key to Solving Real-Life Challenges in Pharma Formulations

Speaker:
Sudharsan Pandiyan, Principal Scientist II, Schrödinger

Abstract:
The demand for innovative drug delivery methods has driven researchers to explore the intricate structure-property relationships within pharmaceutical formulations. Quantum Mechanical (QM) and Molecular Dynamics (MD) simulations are powerful tools for understanding these formulations at a molecular level. Key areas of interest in pharmaceutical sciences include chemical stability, reactivity, molecular degradation, impurity profiling, excipient selection, and polymorph prediction. A thorough understanding of the Active Pharmaceutical Ingredient (API) is essential before embarking on the formulation development process. The Schrödinger Materials Science Suite (MS-Suite) offers comprehensive computational workflows to predict spectra (IR, Raman, NMR, UV-Visible, XRD) and assess the API’s behavior under varying pH conditions, including its degradation pathways and chemical reactivity. Recent advancements in GPU technology have significantly accelerated MD simulations, enabling previously unattainable time scales. This dramatic speedup, combined with predictive accuracy, is poised to revolutionize the use of MD simulations in pharmaceutical formulation development. MD-based workflows can help us address critical formulation design questions on physical stability of formulations, phase transitions, miscibility, solubility and diffusion of API through membranes, morphology, excipient compatibility, encapsulation, and coating selections. This presentation will highlight several successful case studies that demonstrate these capabilities from a molecular perspective.

icon time SEPT 18 | 18:00 IST
Harnessing Molecular Modeling to transform innovation in Polymeric Materials and Consumer Packaged Goods

Speaker:
Sriram Krishnamurthy, Senior Scientist I, Schrödinger

Abstract:
Polymeric materials and consumer packaged goods (CPGs) hold significant importance in both industrial and everyday contexts, impacting numerous aspects of modern life. Polymers play a crucial role in various industries due to their versatility and wide range of applications like construction, electronics, healthcare, and automotive. As scientific research and innovation continue to advance, polymeric materials remain at the forefront of development. In the context of consumer packaged goods (CPG), which significantly influence our daily lives, there is an ongoing push to create products that meet evolving consumer demands, such as healthier food options and more sustainable packaging solutions. Polymeric materials and CPGs are deeply interconnected in their importance to modern society. Molecular modeling has emerged as a transformative tool in the design and optimization of these materials. By providing deep insights into molecular interactions and material properties, molecular modeling accelerates the development of novel, efficient, and sustainable materials. This approach not only enhances our understanding of material behavior, but also facilitates the innovation of advanced solutions tailored to the specific needs of modern consumer products. This webinar will highlight Schrödinger’s Materials Science tools that can accelerate R&D efforts in these scientific domains. We will showcase practical case studies to tackle key problems and identify areas where molecular modeling can be applied.

icon time SEPT 25 | 18:00 IST
Efficient Computation of Process Parameters for Controlling the Chemistry of Deposition or Etch

Speaker:
Simon Elliott, Research Leader, Schrödinger

Abstract:
We present a variety of computational techniques for understanding, controlling and improving deposition and etch processes. The emphasis is on choosing the right technique for the research question and time available. The same computational techniques can be used to investigate other gas-surface processes, such as catalysis or sensing. Different chemical processes can be in competition when a solid surface is treated with a gaseous reagent and the outcome is determined by conditions such as temperature and pressure. For instance, continuous deposition (CVD) may take over from self-limiting deposition (ALD) as the temperature is raised. Or temperature may dictate which material is deposited; in the case presented here, ruthenium oxide film is deposited from RuO4+H2 in experiments at 75°C, whereas Ru metal is obtained at 100°C and above. Ru is being investigated as an electroplating seed layer in electronics, as a capacitor electrode and as a heterogeneous catalyst – all applications that require metal rather than oxide. We show that thermodynamics based on density functional theory (DFT) is a computationally-efficient approach for distinguishing between the possible surface-gas processes. The temperatures and pressures for crossover between different chemistries can be estimated, with the accuracy depending on how entropy, coverage and diffusion are treated. We use DFT to examine the conditions of stability for Ru metal, hydride, hydroxide and oxide with respect to H2 and RuO4 reagents, and so explain the crossover from oxide to metal film just below 100°C. We point out how to balance the cost (in terms of researcher time and computer time) against the benefit that each level of accuracy can offer. In the second part of the talk, we introduce Microkinetic Modelling, a new Schrödinger capability for examining the overall kinetics of gas-surface chemistry by solving the coupled kinetic rate equations of its constituent elementary reaction steps. This allows the simulation of macroscopic parameters such as sticking coefficients that can be experimentally measured and used as inputs for fluid dynamics simulations. We first outline the computational scheme, where elementary steps and their activation free energies have been computed with DFT. The resulting microkinetic model for alumina ALD yields measurable quantities (e.g. growth rate) as a function of temperature and pressure, which are validated against experiment. Variation with pressure can account for penetration depth and conformality within high aspect ratio features. The two cases discussed in this talk thus illustrate how atomic-scale DFT can be embedded into higher-level computational schemes for accurate and achievable prediction of the conditions and parameters for controlling chemical processes.

icon time OCT 1 | 18:00 IST
How Physics-based Modeling and Machine Learning Enable Accelerated Development of Battery Materials

Speaker:
Garvit Agarwal, Senior Scientist II, Schrödinger

Abstract:
The rapid advancements in rechargeable Li-ion battery (LIB) technology over the last decade has revolutionized several key industries such as transportation and consumer electronics. However, new battery chemistries are needed to meet the rapidly growing demand and to improve the power density, safety, reliability, and lifetime of LIBs. Molecular modeling has become an integral part of the design cycle of new battery chemistries. Accurate physics-based modeling enables rapid evaluation and screening of large chemical and material design space thereby, helping industries reduce the time required to bring the new technology to the market. In this webinar, we will introduce the latest technological innovations in Schrödinger’s digital chemistry platform for battery materials design. In particular, the webinar will focus on examples to demonstrate the application of automated solutions for accurate prediction of thermodynamic stability and voltage profile of cathode materials, ion diffusion pathways and kinetics in electrode materials, transport properties of liquid electrolytes and modeling the nucleation and growth of solid electrolyte interphase (SEI) layers using Schrödinger’s SEI simulator module. We will also introduce an automated generalized framework for the development of customized machine learning force fields for complex materials such as liquid electrolytes, inorganic cathode coatings and solid polymer electrolytes, paving the way for efficient design of novel materials for next generation batteries.

icon time OCT 8 | 18:00 IST
Accelerating the Design of Asymmetric Catalysts with Schrödinger’s Digital Chemistry Platform

Speaker:
Saientan Bag, Senior Scientist I, Schrödinger

Abstract:
Asymmetric catalysis has become an integral part of the science-driven technological revolution in the second half of the 21st century, leading to decreased energy demands, sustainable chemical processes and the realization of “impossible” transformations. Asymmetric catalysis based on chiral transition-metal complexes plays an important role in the synthesis of single-enantiomer drugs, perfumes and agrochemicals. The importance of the field is recognized by two Nobel Prize Awards in 2001 (transition-metal catalysis) and 2021 (organocatalysis). Asymmetric catalysts are traditionally designed by experimental trial-and-error methods, which are resource-, time- and labor-consuming, and thus extremely expensive. Digital methods offer the opportunity to expedite catalyst design. Until recently, computational chemistry, typically quantum chemical studies, indirectly contributed to asymmetric catalyst design by providing rationalization for the mechanism of generation of chirality. With the development of more advanced methods, algorithms and an included layer of automation, computational catalysis is now providing the possibility for direct asymmetric catalyst design. In this webinar, I will demonstrate how Schrödinger’s advanced digital chemistry platform can be used to accelerate the direct design and discovery of asymmetric catalysts.

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AI/ML meets physics-based simulations: A new era in complex materials design

Posted on August 14, 2024November 6, 2025 by Anonymous
SEP 27, 2024

AI/ML meets physics-based simulations: A new era in complex materials design

The simulation of materials properties using physics-based approaches, such as density functional theory (DFT) and molecular dynamics (MD), has long been successful in providing insights into structure-property relationships and subsequently aiding in the design of novel materials. In recent years, AI/machine learning (ML) has been used extensively in conjunction with physics-based modeling techniques to greatly accelerate materials innovation. The accuracy and generalizability of physics-based modeling improves the performance of AI/ML models and enables them to be used effectively even in small-data regimes. Conversely, the speed and flexibility of AI/ML help bridge the time- and spatial- scale limitations of physics-based models, creating a synergistic approach that optimizes both predictive accuracy and computational efficiency.

In this webinar, we demonstrate the application of this combined approach in designing materials and formulations across diverse materials science applications, from battery electrolytes and fuel mixtures to thermoplastics and OLED devices.

Key Learning Objectives:

  • Understand how DFT descriptors enhance the accuracy of AI/ML models for optoelectronic molecules and battery electrolytes
  • Discover how MD simulation descriptors improve AI/ML models for the viscosity of organic molecules
  • Explore the use of Schrödinger’s automated Formulation Machine Learning solution to:
    • Train AI/ML models for the solubility of APIs in binary solvents
    • Predict the motor octane number of hydrocarbons
  • Learn about advances in AI/ML force field technology (QRNN) and its application in modeling the bulk properties of inorganic cathode coating materials

Our Speaker

Anand Chandrasekaran

Senior Principal Scientist, Schrödinger

Anand Chandrasekaran joined Schrödinger in 2019 and he is currently the Product Manager of MS-Informatics. His expertise is in applying machine learning to different areas in Materials Science and computational modeling. He graduated from the group of Prof.Nicola Marzari in the Swiss Federal Institute of Technology, Lausanne with a PhD in Materials Science. Before joining Schrödinger, Anand also worked in the group of Prof. Rampi Ramprasad on a number of topics including polymer informatics, machine-learning force-fields, and machine-learning for electronic structure calculations.

HomeCare & Beauty Sustainability Summit 2024

Posted on August 9, 2024August 16, 2024 by Anonymous
Conference

HomeCare & Beauty Sustainability Summit 2024

CalendarDate & Time
  • September 11th-12th, 2024
  • 17:00 CET
LocationLocation
  • Amsterdam, Netherlands
Register

Schrödinger is excited to be participating in the HomeCare & Beauty Sustainability Summit 2024 conference taking place on September 11th – 12th in Amsterdam, Netherlands. Join us for a presentation by Jeffrey Sanders, Product Manager and Scientific Lead of Consumer Goods at Schrödinger, titled “Beyond AI: Leveraging physics-based modeling and machine learning to develop sustainable personal care products.” Stop by booth R4 to speak with Schrödinger scientists.

icon time SEPT 11 | 17:00 CET
icon location 4 Sustainable HomeCare Products Forum
Beyond AI: Leveraging physics-based modeling and machine learning to develop sustainable personal care products

Speaker:
Jeffrey Sanders, Product Manager and Scientific Lead of Consumer Goods, Schrödinger

Abstract:
The journey to develop and reformulate products to become more sustainable has many challenges. Research and development in these areas often demand substantial time, resources, and new raw materials. To accelerate this process, predictive modeling offers the potential to identify promising ingredients, formulations, and even new packaging materials that meet sustainability requirements. A major obstacle in building and deploying useful models is data sparsity. One promising avenue to explore is multi-scale physics-based simulations, as they do not require large experimental datasets as inputs and can be combined with sparse existing data to generate more robust models. This talk will highlight a case study where physics-based simulation was used to accelerate the development of an eco-friendly personal care formulation, and also how molecular-level simulation can be incorporated into machine learning models when little experimental information is available.

Register

Computationally-Guided Drug Formulation Webinar Series

Posted on August 8, 2024August 28, 2026 by Anonymous
Webinar

Computationally-Guided Drug Formulation Webinar Series

CalendarDate & Time
  • September 11th, 2024 – June 3rd, 2026
LocationLocation
  • Virtual

A smart, strategic drug formulation can efficiently advance your drug development projects and inform downstream processes. Advances in molecular modeling and machine learning are enabling atomistic-level insights and the ability to evaluate large numbers of candidate materials and formulations prior to experiments.

Computationally-Guided Drug Formulation Webinar Series – Seven webinars in which we demonstrate how the latest computational modeling tools are impacting the various steps in the pharmaceutical formulation process. In each webinar we feature an expert from Schrödinger sharing valuable insights and practical applications on a key topic. Watch the recordings to learn how to optimize your formulation process with structure-based insights and efficient parameter screening.

  • June 3, 2026
    A predictive modeling platform for studying degradation, reactivity, and catalysis of small molecule active pharmaceutical ingredients
    Speakers: Pavel Dub, Research Leader and Product Manager, Catalysis & Reactivity Schrödinger
    Shiva Sekharan, Global Portfolio Leader of Formulations/CSP, Schrödinger
    Watch now
  • May 12, 2026
    Accelerating amorphous solid dispersion (ASD) formulation with Schrödinger’s Materials Science Suite
    Speakers: Ben Coscia, Principal Scientist II, Materials Science Modeling Services, Schrödinger
    Shiva Sekharan, Global Portfolio Leader of Formulations/CSP, Schrödinger
    Watch now
  • May 14, 2025
    Computational insights into polymer excipient selection for amorphous solid dispersions
    Speaker: Andrea Browning, Senior Director for Polymers
    Watch now | Watch now (with Chinese subtitles)
  • April 8, 2025
    Accelerating pharmaceutical formulations using machine learning approaches
    Speaker: Anand Chandrasekaran, Senior Principal Scientist
    Watch now | Watch now (with Chinese subtitles)
  • November 6, 2024
    Modeling lipid nanoparticles: Self-assembly and apparent pKa calculation
    Speaker: John Shelley, Fellow
    Watch now | Watch now (with Chinese subtitles)
  • October 23, 2024
    Crystal structure prediction workflow for small molecule drug formulation
    Speaker: Lingle Wang, Sr. Vice President, Scientific Development
    Watch now | Watch now (with Chinese subtitles)
  • October 9, 2024
    Molecular-level insight into solubility-enhancement via cosolvents and amorphous solid dispersions
    Speaker: Ben Coscia, Principal Scientist
    Watch now | Watch now (with Chinese subtitles)
  • September 25, 2024
    Computational reactivity and catalysis for drug synthesis
    Speaker: Michael Rauch, Associate Director, Materials Science
    Watch now | Watch now (with Chinese subtitles)
  • September 11, 2024
    Characterizing small drug-like molecules with automated computational spectra prediction
    Speaker: Art Bochevarov, Research Leader
    Watch now | Watch now (with Chinese subtitles)

2024 International Elastomer Conference

Posted on August 5, 2024August 5, 2024 by Anonymous
Conference

2024 International Elastomer Conference

CalendarDate & Time
  • September 9th-12th, 2024
LocationLocation
  • Pittsburgh, Pennsylvania
Register

Schrödinger is excited to be participating in the 2024 International Elastomer Conference taking place on September 9th – 12th in Pittsburgh, Pennsylvania. Join us for a presentation by Manav Bhati, Senior Scientist II at Schrödinger, titled “Design of Elastomers with Tailored Thermal Properties Using Molecular Modeling and Machine Learning.” Stop by booth 918 to speak with Schrödinger scientists.

icon time Sept 11 | 4:30 PM
icon location Expo Hall C
Design of Elastomers with Tailored Thermal Properties Using Molecular Modeling and Machine Learning

Speaker:
Manav Bhati, Senior Scientist II, Schrödinger

Abstract:
Elastomers are integral to industrial applications because of their useful material properties, such as elasticity, durability, and versatility. Innovating new elastomers can lead to the development of superior products that are more durable and stable. A key thermal property of elastomers is the glass transition temperature (Tg), which indicates the temperature at which an elastomer transitions from a glassy/hard state to a soft/rubbery state. Tg is a critical parameter because it determines an elastomer’s operability and performance at various temperatures. Traditional experimental techniques for determining Tg are time-consuming and expensive, necessitating computational approaches to accelerate the design of new elastomers and minimize the failure rate of resource-intensive experimentation. This study focuses on using machine learning (ML) and classical molecular dynamics (MD) simulations to predict the Tg of elastomers. Using curated datasets of experimental Tg values for homopolymers and copolymers from literature, we developed predictive ML models that can accurately predict Tg for new elastomers that are outside of the dataset used to train the model. We then use these ML models to efficiently screen the design space of elastomers by enumerating a large library of copolymer systems. The Tg of the top-performing elastomers were then validated using MD simulations, which have been shown previously to accurately capture the experimental Tg trends of polymer systems. This computational modeling approach not only accelerates the development of new elastomers, but it also provides insights into the relationship between chemical structure and composition to thermal properties.

Register

MS Reactive Interface Simulator

MS Reactive Interface Simulator

Generate physically relevant electrode-electrolyte interface morphologies for batteries

Request a DemoView Documentation
MS Reactive Interface Simulator

Overview

MS Reactive Interface Simulator enables rapid modeling of solid electrolyte interphase (SEI) nucleation and growth in batteries using a template-based reaction approach, and offers atomistic insights into the composition and morphology of this complex battery component. Coupled with Desmond, Schrödinger’s high-speed GPU-based molecular dynamics (MD) engine, and the OPLS force field, MS Reactive Interface Simulator facilitates efficient analysis of electrolyte chemistries by generation of realistic SEI morphologies.

Key Capabilities

Accelerate physically realistic SEI formation with GPU-accelerated MD
Execute reactions using predetermined templates
Enable exploration of multiple chemistries under varying conditions with SMARTS based reaction templates
Employ advanced analysis tools to characterize morphology and understand the properties of the SEI layer

Related Resources

Electrodes, electrolytes & interfaces: Harnessing molecular simulation and machine learning for rapid advancements in battery materials development

Watch Webinar

Taking experimentation digital: Materials innovation using atomistic simulation and machine learning at-scale

Watch Webinar

Energy Capture and Storage

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Documentation & Tutorials

Get answers to common questions and learn best practices for using Schrödinger’s software.

Materials Science Documentation

MS Reactive Interface Simulator

Generate physically relevant electrode-electrolyte interface morphologies for batteries.

View more tutorials

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Broad applications across
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and accurate in silico molecular prediction.

Polymeric Materials
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Publications

Browse the list of peer-reviewed publications using Schrödinger technology in related application areas.

Materials Science Publication

Data-Driven Design of Organic Semiconductors Exhibiting Low Reorganization Energy via Hierarchical Variational Autoencoders, Gaussian Mixture Regression, and Bayesian Optimization

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RedCat, an automated discovery workflow for aqueous organic electrolytes

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Band Gap and Reorganization Energy Prediction of Conducting Polymers by the Integration of Machine Learning and Density Functional Theory

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Designing polymersomes with surface-integrated nanoparticles through hierarchical phase separation

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Synthesis, optical and electrochemical properties of thiophene and thieno [3, 2-b] thiophene linked with structurally modified rhodanine based copolymers

Materials Science Publication

Stability enhancement of Amphotericin B using 3D printed biomimetic polymeric corneal patch to treat fungal infections

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Advancing efficiency in deep-blue OLEDs: Exploring a machine learning–driven multiresonance TADF molecular design

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Conformers influence on UV-absorbance of avobenzone

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Synthesis, computational studies and evaluation of benzisoxazole tethered 1,2,4-triazoles as anticancer and antimicrobial agents

Materials Science Publication

Unveiling a Novel Solvatomorphism of Anti-inflammatory Flufenamic Acid: X-ray Structure, Quantum Chemical, and In Silico Studies

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Training & Resources

Online certification courses

Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.

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Hit Discovery Services 

Hit Discovery Services

Hit Discovery Services

Find more diverse hits, faster

Speak to an expertview all services

Overview

Enable your drug discovery program with Schrödinger’s unrivaled technologies and deep expertise. We’ll give your hit discovery campaigns the best chances of success by leveraging our team of experts and our most advanced technologies for ultra-large virtual screening and rigorous rescoring at scale.

Propel your discovery program with unrivaled technologies and expertise

Benefit from the full impact of Schrödinger’s hit discovery capabilities

• Our team of experts use extensively validated screening and rescoring workflows that leverage Schrödinger’s latest technologies deployed at scale
• Service includes all computing, licensing, and service hours to perform a cutting-edge hit identification campaign with no upfront licensing or hardware costs

Obtain more and higher quality hits with unique rescoring technologies

• Promising compounds are rescored with unmatched accuracy using ABFEP+ amplified by machine learning
• Accurately identify more diverse and potent hits, requiring fewer compounds to be purchased and assayed

Maximize novelty and diversity by screening billions of compounds

• Screen commercial libraries of >5 billion compounds (or >300M for fragments screens) using both structure- and ligand-based approaches simultaneously to maximize the number of unique hits identified
• Explore the largest commercially available libraries for rapid and reliable procurement, including Enamine REAL and WuXi LabNetwork
• Efficiently screen your proprietary or sculpted libraries to explore alternative chemical spaces

From Feasibility Study to Purchase List

Schrödinger has over 20 years of scientific experience in developing industry-leading virtual screening technologies which are used broadly in pharmaceutical companies worldwide.

Through continuous methodology development effort combined with extensive deployment in active drug discovery programs across diverse targets, our team of computational experts have optimized an advanced virtual screening workflow offered in the Hit Discovery Service.

Scientifically-validated solutions for virtual screening

  1. Efficient Exploration of Chemical Space with Docking and Deep Learning.

    Yang et al. J. Chem. Theory Comput. 2021, 17(11), 7106-7119.

  2. Enhancing Hit Discovery in Virtual Screening through Absolute Protein–Ligand Binding Free-Energy Calculations.

    Chen et al. J. Chem. Inf. Model. 2023, 63, 10, 3171–3185.

  3. Benchmarking Refined and Unrefined AlphaFold2 Structures for Hit Discovery.

    Zhang et al. J. Chem. Inf. Model. 2023, 63, 6, 1656–1667.

  4. WScore: A Flexible and Accurate Treatment of Explicit Water Molecules in Ligand–Receptor Docking.

    Murphy et al. J. Med. Chem. 2016, 59, 9, 4364–4384.

Software and services to meet your organizational needs

Software Platform

Deploy digital drug discovery workflows using a comprehensive and user-friendly platform for molecular modeling, design, and collaboration.

Modeling Services

Leverage Schrödinger’s computational expertise and technology at scale to advance your projects through key stages in the drug discovery process.

Support & Training

Access expert support, educational materials, and training resources designed for both novice and experienced users.

Release 2024-3

Library Background

Release Notes

Release 2024-3

Small Molecule Drug Discovery

Platform Environment

Maestro Graphical Interface

  • Create customizable histograms from numerical data that are automatically synchronized with selection or filtering in other charts, the Project Table, or Workspace
  • Improved support for T-Cell Receptors with display of their annotations in the Structure Hierarchy

Force Field

  • Full release of the OPLS5 polarizable force field for organic atoms for improved FEP+ and Desmond model accuracy

Workflows & Pipelining [KNIME Extensions]

In LiveDesign:

  • Ability to use a single generic protocol regardless of model input columns
  • LiveDesign connection node can take credentials from the session rather than storing them in the workflow
  • Date type columns are supported as LiveDesign model input

Binding Site & Structure Analysis

SiteMap

  • Enable compact mode for sites with volume larger than a cutoff
  • New RNA mode for improved performance of SiteScore for RNA

Desmond Molecular Dynamics

  • New Unbinding Kinetics workflow to gain insights into drug-target residence time and optimize in vivo efficacy, safety profiles, and ADMET (beta)
  • Analyze halogen bonds in SID Panel
  • View local strain energy in “Torsion” tab of SID Panel

Mixed Solvent MD (MxMD)

  • Improved organization of output structures and data in prjzip file

Hit Identification & Virtual Screening

  • Streamline visualization of hits in the Hit Analyzer by outputting VSDB per docking run by default
  • Streamlined generation of WScore models with new WScore Quick Model Generation panel (beta)

Ligand Preparation

Hit Analysis

  • Filter chemotypes by SMARTS in Hit Analyzer Panel

FEP+

  • Improved management of pKa/tautomer/conformer ensembles on ABFEP systems with Groups tab
  • Core-SMARTS selection no longer requires selecting explicit hydrogen atoms
  • Improved user interface allows more intuitive column sorting
  • Export to LiveDesign now includes additional fields
  • Edge analysis now includes halogen protein-ligand interactions
  • Guided access to open FEP+ Panel for analysis upon calculation completion via Workflow Action Menus (WAM) in Maestro

Protein FEP

  • New lambda dynamics (λD) enhanced protein residue mutation FEP+ for identifying high quality protein variants (beta)
  • Expanded OPLS5 support for “Protein FEP” and “Protein FEP for Ligand Selectivity” panels

Solubility FEP

  • Expanded OPLS5 support for Solubility FEP simulations

FEP Protocol Builder

  • Gain up to 35% speedup in calculations due to changed defaults in the FEP Protocol Builder panel

Biologics Drug Discovery

  • Perform DNA/RNA nucleobase mutations using residue scanning on command line via mut-pred.py
  • Analyze DNA/RNA interactions with proteins in the Protein Interaction Analysis panel
  • Search the non-standard residues library and find the closest matching natural amino acid analog
  • Automatically annotate and number T Cell Receptor (TCR) structures using IMGT or AHo schemes
  • Use pose-viewer files as input for Protein Interaction Analysis

Materials Science

GUI for Quantum ESPRESSO

Product: Quantum ESPRESSO (QE) Interface

  • Check for the number of irreducible k-points from the panel
  • Upgrade to Quantum ESPRESSO 7.3.1
  • Quicker assessment of electric field for faster phonon calculations
  • Force and stress information reported in the project table
  • Option for more diagonalization algorithms for GIPAW steps (command line)
  • Option to set separate driver and subjob hosts for NEB calculations
  • Solid State NMR Viewer: Improved UI for selecting elements

Transport Calculations via MD simulations

Product: MS Transport

  • Diffusion: Support for non-orthorhombic systems as input

Materials Informatics  

Product: MS Informatics

  • Formulation ML: Option to use Machine Learning Property predictions as descriptors
  • Formulation ML: Option to use DeepAutoQSAR predictions as descriptors
  • Machine Learning Property: Updates to existing models
  • Machine Learning Property: Prediction of S1-T1 energy gap
  • Machine Learning Property: Prediction of aqueous solubility
  • Machine Learning Property: Output entries separated for each solvent

Coarse-Grained (CG) Molecular Dynamics

Product: MS CG

  • Coarse-Grained Force Field Builder: Automated mapping for dissipative particle dynamics (DPD)
  • Coarse-Grained Force Field Builder: Visualization of CG mapping in the workspace

Reactivity

Product: MS Reactivity

  • Nanoreactor: Frames from MD trajectory added to list of products
  • Nanoreactor: Support for multistate (e.g. singlet-triplet) reactions
  • Nanoreactor: Number of loaded structures reported in the viewer
  • Nanoreactor: Plot for reactants (red) shown with products (blue) in the viewer
  • Nanoreactor: Reactant structures to be included as standard output
  • Reaction Workflow: Support for AutoTS output as input

Microkinetics

Product: MS Microkinetics

  • Microkinetic Modeling: Support for renaming of reactions and participating species
  • Microkinetic Modeling: Automatic population of molecular weight for gas/solute species
  • Microkinetic Modeling: Automatic assigning of collision factor based on reaction type

MS Maestro Builders and Tools

  • Solvate System: Option to neutralize systems with built-in counterions

Classical Mechanics

  • Barrier Potential for MD: Support for NPT ensemble
  • Elastic Constants: Option to reset the viewer panel
  • Meta Workflows: Support for trajectory-based free volume analysis
  • Order Parameter: Option to compute acentric order parameter
  • Polymer Crosslink: Option to use a barrier potential
  • Polymer Chain Analysis: Support for molecules with less than 40 atoms

Quantum Mechanics

  • Adsorption Energy: Option to constrain atomic positions for systems with PBC
  • Optoelectronic Film Properties: Workflow solution encompassing transition dipole moment orientation and singlet excitation energy transfer (SEET) calculations

Education Content

Life Science

  • New Tutorial: Introduction to MD Trajectory Analysis with Desmond
  • New Tutorial: Re-scoring Docked Ligands with MM-GBSA
  • Updated Tutorial: Understanding and Visualizing Target Flexibility
  • Updated Tutorial: Approximating Protein Flexibility without Molecular Dynamics

Materials Science

  • New Tutorial: Singlet Excitation Energy Transfer
  • New Tutorial: FEP Solubility
  • New Tutorial: Genetic Optimization
  • New Tutorial: Adsorption of Panthenol on Skin with All-Atom Molecular Dynamics
  • Updated Tutorial: Applying Barrier Potentials for Molecular Dynamics Simulations
  • Updated Tutorial: Automated Dissipative Particle Dynamics (DPD) Parameterization
  • Updated Tutorial: Design of Asymmetric Catalysts with Automated Reaction Workflow
  • Updated Tutorial: Machine Learning Property Prediction
  • Updated Tutorial: Crosslinking Polymers

LiveDesign

What’s new in 2024-3

  • Uploads from Maestro to LiveDesign could fail if the LiveDesign project had more than 32,000 columns, and now complete successfully regardless of the number of columns
  • LiveReports that contained columns with many values would show red error bars at the top of the LiveReport, and now no longer show the red error bars
  • LiveReport tabs would disappear after logging out and logging in, and now correctly appear after logging back in
  • New LiveDesign Learning module for rapid AI/ML molecular property predictions: Enables highly scalable, automated AI/ML pipelines for drug design
    • *LiveDesign Learning is now called LiveDesign ML
  • Accelerated scaffold and R-group design with AutoDesigner Core Design: Automatically generate and optimize novel cores and R-group(s) simultaneously
  • Delete Published Freeform column and Formula columns from the Data & Columns Tree
  • Biologics:
    • Sequence-activity relationships in the Sequence Viewer:
      • Ability to add a quantitative column from the LR in the viewer
      • Correlate the changes in the residues and the activity data with the heatmap
    • There would only be one option when trying to import the Biologics data via csv and the option “Import As Single Entity for CSV” won’t show now.
    • Performance of structure hierarchy loading and item selection through hierarchy panel in the 3D Visualizer are improved.
    • Double-clicking an item in the hierarchy zooms to that selection in the 3D Visualizer workspace.
    • Set gap penalties in the sequence viewer to generate more useful alignments
  • Landing Pages:
    • The Landing page now links to a specific URL and enable bookmarking the Landing Page in a browser
    • Download resources and files from the Landing Page Resource page
  • Spreadsheet View:
    • A warning message alerting the user to expect decreased performance now appears on LiveReports that contain more than one million cells
    • Entity images no longer enlarge when hovering over the image, and can now be zoomed by clicking a magnifying glass button that appears to the right of the entity image
  • The User details page in the Admin Panel now shows a warning that unlicensed usernames will not appear in dropdown lists throughout LiveDesign
  • Models now support date and datetime returns
  • Forms Matrix Widget now render larger editing areas for Freeform column cells when the cells are small

What’s Been Fixed

  • LiveReports would show red error bars when multiple input values to a parameterized model changed simultaneously in the spreadsheet, and now the LiveReport loads correctly
  • Popping out a model column’s cell that contained and image would open two tabs in the browser (one tab with the image, and one blank tab), and now only opens a tab with the image
  • LiveReports would occasionally lose their filters, and the filter panel would appear blank, but no longer lose their filters
  • Changing a user’s role within a Single Sign-on Identity Provider would not update the user’s role within LiveDesign when they logged out and logged back in, and now the role changes are correctly used after the user logs out of LiveDesign and logs back in
  • Changes to parameterized model in the Admin Panel (e.g., the Title or Folder) would not save after clicking the Save button, and now correctly save and update the parameterized model
  • Changes to a “set fixed” protocol parameter get passed along to the dependent model or parameterized model without breaking them.
  • The Formula Substructure Search function incorrectly reported the count of substructure matches as 1, even if there were multiple matches, and now correctly reports the total number of substructure matches
  • Adding a new project with an identical name to an archived project provided a cryptic error message, and now provides a clear message instructing the user to choose a different name
  • When many LiveReports were open, the active LiveReport tab would disappear when left-side panels were opened, and now the active LiveRepot tab remains visible
  • Newly created models would not inherit the Recalculate Model option defined in the protocol, and would default to the “Automatically” option, and now the models correctly inherit the option defined in the protocol
  • Parameterized models that have had their columns renamed in the Admin Panel would show the old, original column names when that model was added to LiveReports, and now correctly show the updated column name
  • The user interfaces of the Filters panel and Advanced Search panel have been unified
  • Changing the column widths within the LiveReport picker caused the column header to misalign with the column contents, and now the header remains aligned
  • The prefix (Global) would appear repeatedly for templates in the Global project that were updated and overwritten, and now templates in the Global project only show a single (Global) prefix after they are updated and overwritten
  • Maestro would not import 3D results from LiveDesign when the 3D column title was renamed, and now correctly imports all 3D data regardless of the column title
  • LiveDesign would occasionally freeze due to a database lock, and now no longer will freeze
  • Opening a model attachment from the main spreadsheet (e.g., a LID from a Glide model) would fail to show the image, and now correctly shows the image
  • Filtering out a frozen row would show flashing squares in the first row in the main spreadsheet, and now correctly shows that row’s data
  • The Project Picker would appear after a five-second delay when there are a large number of projects to show, and now the Project Picker appears instantly
  • The sequence viewer would occasionally show incorrect colors and tooltips for non-natural amino acids, and now shows the correct information
  • Hovering over a residue in the sequence viewer would cause the viewer to scroll to the top, and now the scroll position remains does not change
  • Plot tooltips could not be dragged and moved after pinning to the screen, and now can be dragged to a new position after pinning
  • Model results would occasionally appear as Failed in the LiveReport, when in fact the model ran successfully, and now model results correctly show results in the LiveReport

Training & Resources

Online Certification Courses

Level up your skill set with hands-on, online molecular modeling courses. These self-paced courses cover a range of scientific topics and include access to Schrödinger software and support.

Tutorials

Learn how to deploy the technology and best practices of Schrödinger software for your project success. Find training resources, tutorials, quick start guides, videos, and more.

Other Resources

NAMES 2024

Posted on July 26, 2024August 2, 2024 by Anonymous
Conference

NAMES 2024

CalendarDate & Time
  • August 8th-9th, 2024
LocationLocation
  • Ann Arbor, Michigan
Register

Schrödinger is excited to be participating in the NAMES 2024 conference taking place on August 8th – 9th in Ann Arbor, Michigan. Join us for a workshop by Katie Dahlquist, Senior Scientist at Schrödinger, titled “Empowering Exploration: A Workshop on Molecular Modeling for Materials Science.”

Speaker:
Katie Dahlquist, Senior Scientist, Schrödinger

Date/Time:
Friday, August 9 | 1:30PM – 3:30PM

Abstract:
The Schrödinger Materials Science platform is a single interface with access to structure building, simulation, and analysis for atomic-scale simulation. With respect to simulation, the platform has extensive capabilities in molecular and periodic quantum mechanics (namely density functional theory calculations), molecular dynamics, and machine learning. This workshop will guide participants to work hands-on with the Schrödinger Materials Science platform. We will instruct attendees through parts of two of our online courses which are best-suited for the NAMES audience: Polymeric Materials and Battery Materials.