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AI in Drug Discovery USA 2024

Posted on October 2, 2024October 2, 2024 by Anonymous
Conference

AI in Drug Discovery USA

CalendarDate & Time
  • October 21st-22nd, 2024
LocationLocation
  • Boston, Massachusetts
Register

Schrödinger is excited to be participating in the AI in Drug Discovery USA conference taking place on October 21st – 22nd in Boston, Massachusetts. Join us for a presentation by Karl Leswing, Executive Director, Machine Learning at Schrödinger, titled “Latest advancements in machine learning-enhanced in silico design: Impact on a pipeline of drug discovery programs.”

Speaker:

Karl Leswing, Executive Director, Machine Learning, Schrödinger

Key Learning Objectives:

  • Using active learning with FEP+ for large-scale in silico fragment screens in hit discovery
  • Applying de novo design workflows for intelligent molecular core design
  • Leveraging experimental data for enhancing ADMET profiles in lead optimization using an interactive ML dashboard

Karl Leswing

Executive Director, Machine Learning, Schrödinger

Karl Leswing is the Executive Director for Machine Learning at Schrödinger. In this role he oversees the research and execution of machine learning applications for Schrödinger’s digital chemistry platform. In 2017 he was a visiting researcher at the Pande Lab working on using deep learning techniques for drug discovery. During that time he co-authored MoleculeNet, a benchmarking paper analyzing machine learning techniques for chemoinformatics. Karl received his undergraduate degree from the University of Virginia, and a Master’s in machine learning from Georgia Tech.

AAPS 2024 PharmSci 360

Posted on September 24, 2024October 1, 2024 by Anonymous
Conference

AAPS 2024 PharmSci 360

CalendarDate & Time
  • October 20th-23rd, 2024
LocationLocation
  • Salt Lake City, Utah
Register

Schrödinger is excited to be participating in the AAPS 2024 PharmSci 360 conference taking place on October 20th – 23rd in Salt Lake City, Utah. Join us for presentations by Schrödinger scientists. Stop by booth #2503 to speak with us.

icon time OCT 21 | 10:00AM – 10:30AM
icon location 251 F Salt Palace Convention Center
Coarse-Grained Modeling of Nucleic Acid-Loaded Lipid Nanoparticle Formulations

Speaker:
Doug Grzetic, Senior Scientist I, Schrödinger

Abstract:
We will start off with a problem statement describing how the complicated nature of lipid nanoparticle formulations makes efficient formulation optimization a challenge. Additionally, the effectiveness of LNP formulations is believed to be strongly correlated to the LNP morphology, but this is difficult to characterize, making predictive, in silico measurements extremely valuable. Then we will provide a brief description of molecular modeling, emphasizing that for LNP self-assembly length-scales (~100 nm) coarse-grained modeling is required. In addition, high-throughput screening studies require that the building of CG models be automated as much as possible. We briefly review techniques for the automation of this process. We demonstrate the application of coarse-grained modeling to RNA-encapsulating LNPs, with a case study focusing on the Pfizer-BioNTech COVID-19 vaccine formulation.

icon time OCT 21 | 3:15PM – 3:30PM
icon location 255 EF Salt Palace Convention Center
Modernize your arsenal of formulation tools with physics-based molecular simulation

Speaker:
Ben Coscia, Principal Scientist I, Schrödinger

Abstract:
The impact of physics-based molecular modeling and simulation on formulation is expanding rapidly with advancement of computer hardware and software algorithms. Cloud-based solutions enable individuals to access the world’s most powerful processors with just an internet connection. Machine learning algorithms continue to be leveraged towards improving the accuracy of our models and to guide high throughput simulation studies towards targeted properties. Despite this progress, one can argue that physics-based simulation is an underutilized technique, in large part due to slow adoption by non-experts. The purpose of this talk is to inform our audience of the accessibility of simulation and empower them to take the first steps towards interrogating their research questions with simulation, with specific emphasis on solubilization. We do this by example, describing two case studies which apply physics-based molecular simulation to gain insight into two different approaches that have direct implications on solubilizing poorly soluble APIs.

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2024 AIChE Annual Meeting

Posted on September 24, 2024September 30, 2024 by Anonymous
Conference

2024 AIChE Annual Meeting

CalendarDate & Time
  • October 27th-31st, 2024
LocationLocation
  • San Diego, California
Register

Schrödinger is excited to be participating in the 2024 AIChE Annual Meeting taking place on October 27th – 31st in San Diego, California. Join us for presentations by Schrödinger scientists. Stop by booth #521 to speak with us.

icon time OCT 28 | 8:00AM – 8:30AM
icon location Hilton San Diego Bayfront Hotel, Sapphire Ballroom E
Accelerating Polymer Design with Targeted Properties Using Machine Learning and Physics-Based Models

Speaker:
Alex Chew, Principal Scientist I, Schrödinger

Abstract:
Designing new, industrially relevant polymers is challenging because of the need to optimize multiple materials’ properties simultaneously, which is expensive and often infeasible using traditional trial-and-error approaches. One possible solution to identifying promising polymeric materials is to employ a combination of machine learning and physics-based tools to screen the polymer design space and provide suggestions for new polymers that meet the criteria for an industrial application. In this work, we demonstrate a workflow that utilizes machine learning and molecular modeling approaches to design new polymers (specifically, polycarbonates) that satisfy five polymer properties, including the glass transition temperature, optical properties, and mechanical properties. Using a relatively modest dataset of fewer than 200 points, we developed quantitative structure-property relationship (QSPR) models to accurately predict the experimental polymer properties given the homo- or co-polymer structures and composition as input. Leveraging these computationally efficient QSPR models, we then screened over ~10,000 polymer structures that were generated through R-group enumeration tools. We used these QSPR predictions to create multi-parameter optimization scores to help down-select the large polymer space to ~10 promising candidates. We validated the predicted properties of the top polymer candidates using classical molecular dynamics simulations and density functional theory, which revealed reliable correlation between physics-based and QSPR approaches. Finally, we validated the computational predictions against experiments, which showed good agreement with QSPR and physics-based models. Our workflow demonstrates the usefulness of combining data-driven and physics-based approaches in designing new polymers given a small dataset, which is broadly useful for scientists interested in leveraging computer-aided strategies to innovate new materials while mitigating the need for extensive trial-and-error experimentation.

icon time OCT 29 | 4:00PM – 4:30PM
icon location Hilton San Diego Bayfront Hotel, Aqua 300 (AB)
Capturing the unmeasurable: How atomistic simulations are bringing understanding to interfacial phenomena

Speaker:
Andrea Browning, Director, Schrödinger

Abstract:
Most materials development at some point must consider an interface. Between adhesives and component parts, between matrix and filler in composite materials, and between atomic layers during assembly, the interface impacts the overall performance of the product. But these surfaces can be difficult to probe experimentally. Atomistic level modeling and simulation techniques such as quantum mechanics and molecular dynamics allows a window into the specific interactions that accumulate into the observed interfacial behavior. As simulation techniques and compute power has grown, we are now better able to explore how interfaces behave. However, there are still challenges remaining such as accounting for reactions at complex, multicomponent interfaces. From battery solid electrolyte interphases to dissolving polymers at tablet interfaces, simulations must capture discrete interactions that are important to the interface in order to be useful. This talk will review examples from various industries in how simulation of interfaces has developed and the role of technology exploration in Dr. Browning’s career evolution.

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Computational Medicinal Chemistry School

Posted on September 18, 2024September 30, 2024 by Anonymous
Conference

Computational Medicinal Chemistry School

CalendarDate & Time
  • October 28th-30th, 2024
LocationLocation
  • Cambridge, Massachusetts
Register

Schrödinger is excited to be a Founding Sponsor at the Computational Medicinal Chemistry School conference taking place on October 28th – 30th in Cambridge, Massachusetts. Join us for a presentation by Andreas Verras, Director at Schrödinger, titled “Beyond Potency: How Modeling can contribute to ADMET with structure based, ligand property, and machine learning approaches.”

icon time OCT 28 | 2:15 – 3:00 PM
Beyond Potency: How Modeling can contribute to ADMET with structure based, ligand property, and machine learning approaches.

Speaker:
Andreas Verras, Director, Schrödinger

Abstract:
Potency optimization is a general first step in drug discovery, but can be quickly overshadowed by other problems that make in vivo studies impossible. Optimizing absorption, metabolism, efflux and off-target toxicities is necessary to generate molecules that can interrogate your mechanism in an animal and ultimately go to the clinic. I will explore modeling approaches to understanding pharmacokinetic data; improving efflux, absorption, and eflux; and modeling some off target data. A combination of structure based, ligand based, and machine learning approaches is presented with some opinion on which problems they are most suited.

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, 2024June 2, 2025 by Anonymous

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

How Schrödinger’s materials science domain experts ensure partner success

Connect with us
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, 2024July 16, 2026 by Anonymous
Webinar

Computationally-Guided Drug Formulation Webinar Series

CalendarDate & Time
  • September 11th, 2024 – May 14th, 2025
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.

  • May 14, 2025
    Computational insights into polymer excipient selection for amorphous solid dispersions
    Speaker: Andrea Browning, Senior Director for Polymers
    Watch now
  • April 8, 2025
    Accelerating pharmaceutical formulations using machine learning approaches
    Speaker: Anand Chandrasekaran, Senior Principal Scientist
    Watch now
  • November 6, 2024
    Modeling lipid nanoparticles: Self-assembly and apparent pKa calculation
    Speaker: John Shelley, Fellow
    Watch now
  • October 23, 2024
    Crystal structure prediction workflow for small molecule drug formulation
    Speaker: Lingle Wang, Sr. Vice President, Scientific Development
    Watch now
  • October 9, 2024
    Molecular-level insight into solubility-enhancement via cosolvents and amorphous solid dispersions
    Speaker: Ben Coscia, Principal Scientist
    Watch now
  • September 25, 2024
    Computational reactivity and catalysis for drug synthesis
    Speaker: Michael Rauch, Associate Director, Materials Science
    Watch now
  • September 11, 2024
    Characterizing small drug-like molecules with automated computational spectra prediction
    Speaker: Art Bochevarov, Research Leader
    Watch now

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