Accelerating pharmaceutical formulations using machine learning approaches
Date & Time - April 8th, 2025
- 8:00 AM PDT / 11:00 AM EDT / 4:00 PM BST / 5:00 PM CEST
Location - Virtual
Machine learning (ML) is revolutionizing pharmaceutical formulation design by enabling data-driven predictions of critical properties such as solubility, viscosity, and stability. Chemistry-informed AI/ML models provide a powerful framework for accelerating materials innovation beyond active pharmaceutical ingredients (APIs) to complex drug formulations. The ability of machine learning to analyze large amounts of data and make predictions about new formulations allows for rapid exploration of vast chemical spaces, significantly reducing the need for traditional trial-and-error experimentation. Automated workflows can integrate chemical composition and molecular structure to generate predictive models, optimizing formulation properties with greater speed and efficiency.
In this webinar, we will demonstrate how Schrödinger’s integrated ML- and physics-based approaches are transforming pharmaceutical formulation design, including:
- How an automated ML workflow, incorporating chemistry and composition, can predict API solubility in binary solvents
- How ML models, augmented with physics-based descriptors, can be used to optimize viscosity predictions of organic molecules for better formulation performance
- How formulation ML tools empower non-experts to design novel drug formulations that satisfy multiple target criteria simultaneously
Our Speakers
Anand Chandrasekaran
Senior Principal Scientist, Schrödinger
Anand Chandrasekaran joined Schrödinger in 2019 and currently serves as the Product Manager for MS Informatics. His expertise lies in applying machine learning across various domains within materials science and computational modeling. He earned his Ph.D. in Materials Science under Prof. Nicola Marzari at the Swiss Federal Institute of Technology, Lausanne. Prior to joining Schrödinger, Anand worked with Prof. Rampi Ramprasad, focusing on polymer informatics, machine learning force fields, and machine learning for electronic structure calculations.
Shiva Sekharan
Senior Director, Schrödinger
Shiva Sekharan, Ph.D., is the Senior Director of Formulations Business Development at Schrödinger and is responsible for driving the business development efforts in the formulations space. Shiva is an experienced business development executive in the CRO and AI-based services and software solutions industry and has several years of experience in managing business accounts, customer relationships, and expectations with clients in the pharmaceutical, agrichemical, and academic industries across the US, Europe, and Asia territories. His expertise lies in identifying new business opportunities among existing customers, devising sales and collaboration strategies for customer expansion,and ensuring top-tier services, products, and knowledge-driven solutions are available 24/7 to customers across the globe. Before joining Schrödinger in 2023, Shiva held a BD role at XtalPi Inc., where he led the US solid-state services unit, worked with departmental heads to establish effective goals, sales targets, outline procedures and best practices, and provide strategic directions to increase revenue. Shiva earned his Ph.D. in Theoretical Chemistry from the University of Duisburg-Essen, Germany, followed by postdoctoral stints at the Max-Planck Institute for Polymer Science, Emory University, Fukui Institute for Fundamental Chemistry, and Yale University. Shiva is an accomplished computational chemist, with strong research expertise in the areas of quantum chemistry and drug discovery (>40 publications, >1100 citations, H-index = 20).