About BAGIM

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.

Saturday, March 27, 2021

Cambridge Crystallographic Data Centre presents...Greg Warren, Doree Sitkoff and Vera Prytkova

A BAGIM/SAGIM joint production.

Thursday, April 15, 2021

4:30 pm PT (UTC-7)

7:30 pm ET (UTC-4)


Sifting through poses: Applying crystal-based conformational health assessments in ligand docking

Doree Sitkoff, Principal Scientist in Computational Chemistry, Discovery Chemistry, Janssen Research and Development

Molecular docking is a primary computational chemistry tool for hypothesizing how organic ligands bind to macromolecular receptor sites. For a variety of reasons, however, docking software packages can sometimes suggest binding models in which the ligand is conformationally strained. A rapid and reliable way to identify such poses would provide added context when assessing docking results. Here, we calculate a ligand geometry fitness score which coarsely estimates the torsional health of a docked pose, as a supplement to the calculated docking score.  The geometry fitness score is based on the Mogul knowledge base of molecular conformations derived from the Cambridge Structural Database (CSD). Applications of the added geometrical score in examining preferred binding poses, and in virtual screening are discussed.

Sifting through the PDB: Would you prefer a diamond or coprolite for that engagement ring?

Gregory Warren, Director of Computational Chemistry, DeepCure

Historically computational chemists have paid little attention to the metrics of X-ray and neutron diffraction crystal structures outside resolution. While resolution is a useful (easily obtained) metric it is not sufficient.  This presentation will discuss metrics that are more applicable for assessing the quality and/or reliability of structures prior to selection for use. Data showing how structure choice affects docking and ligand strain estimates will be presented.

Structural databases in drug discovery: extracting useful information from the CSD and the PDB

Vera Prytkova, Research and Applications Scientist, CCDC

Knowledge of molecular conformation and interactions derived from small-molecule and protein structures can have significant impact in drug discovery. Structural databases can be mined to identify patterns of interaction or potential scaffold hops to design novel motifs and retrieve a diversity of ligand topologies. Statistically significant information about molecular conformations and intermolecular interactions can help a researcher evaluate the probability of observing a particular conformation of a newly designed drug in the binding site.  At last, conformational analysis of a potential drug candidate allows to perform the stability analysis for solid form development. In this presentation specific aspects and examples of insights derived from structural databases useful for drug discovery efforts will be presented.

Tuesday, January 12, 2021

Steven Kearnes: Pursuing a Prospective Perspective

 Join us on Tuesday, January 26, 2021 - 12:00 PM to 1:30 PM EST

We spend a lot of time building models and comparing them to other models. The field generally agrees that forward-looking predictions are the best validation, but even prospective validation can be misleading if it does not consider the actual deployment context: How does the model affect compound selection? What controls are in place to ensure that downstream uses of model predictions are consistent? As a concrete example, I'll review our recent work with DNA-encoded libraries. I'll conclude with a summary of the Open Reaction Database, a large-scale data gathering effort with some interesting possibilities for prospective applications.

Presentation https://youtu.be/6IDKEIln1JM