BAGIM is an active community of Boston area scientists bringing together people from diverse fields of modeling and informatics to impact life and health sciences. BAGIM strives to create a forum for great scientific discussions covering a wide range of topics including data management, visualization, computational chemistry, drug discovery, protein structure, molecular modeling, structure-based drug design, data mining, software tools, and the sharing of goals and experiences. Our community is made up of participants from academia, government, and industry whose goal is to engage in the discussion of science involving a synthesis of theory and technology. Discussions sponsored by BAGIM are targeted to the needs and interests of informatics scientists, computational chemists, medicinal chemists, and statisticians. BAGIM also provides opportunities for networking within these disciplines as well as an arena for the dissemination of information of specific interest to the membership.

Wednesday, May 24, 2023

Robert Abel - Tight integration of FE, ML, & de novo design in predictive models

 We are happy to announce a in-person meeting sponsored by Schrodinger. We welcome Robert Abel: Executive Vice President, Chief Computational Scientist at Schrödinger.

We will be hosting the event at the Schrodinger located at One Main Street, Cambridge MA 02142. The meeting will be held in the conference room on 1st floor. We have a max capacity of 50 people. A zoom link will be posted here on the day of the meeting for those that cannot attend in person or do not arrive early enough to get into the door. Reception will be held on 11th floor in the Schrodinger office. Reception is only available for in-person attendees of Robert's talk.

Zoom invite link: https://schrodinger.zoom.us/j/98786243995?pwd=SWgyMGFPeUtucXcvY0Q0Y2hUaEVsUT09
Passcode: 364189

Title: Tight integration of free energy calculations, machine learning methods, and de novo design techniques to greatly increase the scale and value of predictive modeling

Abstract: A central challenge in small molecule drug discovery is the need to identify ligands exhibiting the potency, selectivity, and ADMET property balance necessary for safety and biological efficacy. Free energy calculations have distinguished themselves as a highly valuable tool to address this challenge, but are computationally prohibitive to apply to the vast numbers of molecules that might be considered by project teams, which has been estimated as likely > 10^50 distinct possibilities. We will discuss a variety of strategies we are pursuing to greatly expand the scale at which such predictive modeling can be applied, and will extensively highlight the crucial roles tight integration of machine learning methods, de novo design techniques, and enterprise informatics solutions can play in achieving positive outcomes. We will also highlight multiple outstanding challenges to achieving a fully satisfactory approach.

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