Research Areas
The Jain Laboratory was part of the University of California, San Francisco for 22 years (1999-2021). It is now the research wing of BioPharmics LLC, an established company that develops software for computer-aided drug discovery.
We focus on the following areas:
- Small-molecule conformer generation, including complex macrocycles
- Molecular docking and protein binding site comparison and analysis
- Three-dimensional molecular similarity and multiple ligand alignment
- Prediction of affinity and pose for biologically interesting molecules
- Improvements to modeling X-ray density, particularly by employing conformational ensembles
Our approach to predictive modeling is different because we respect physics, chemistry, and biology while also taking machine learning and parameter optimization seriously.
What’s New?
JMC: Bound Ligand Strain
January 27, 2023
We have published a comprehensive study of bound ligand strain in J. Med. Chem., with our colleagues from Merck. Ligand strain, when properly estimated using real-space refinement, is generally quite low. But it grows more rapidly than ligand size and follows a simple distributional model. The super-linearity of ligand strain w/r/t size explains a part of the challenge in designing larger ligands that are highly efficient.
JCAMD: The eSim Similarity Method
October 24, 2019
We have published, with Stephen Johnson, a new 3D molecular similarity method (called eSim) that directly incorporates Coulombic field comparison with surface-shape and hydrogen-bonding comparison. It is both faster and more accurate that commonly used alternatives, both for virtual screening and pose prediction. The paper is entitled “Electrostatic-field and surface-shape similarity for virtual screening and pose prediction.”
JMC: xGen Papers
March 17, 2021
We have published a second paper with colleagues from Merck looking at peptide macrocycle strain energetics in the context of xGen refined conformational ensembles. This follows the paper entitled “XGen: Real-Space Fitting of Complex Ligand Conformational Ensembles to X‐ray Electron Density Maps.” We show that conformational ensembles, without atom-specific B-factors, are better models for ligands in terms of both fit to X-ray density and strain energy.
JCAMD: QuanSA Affinity Prediction Method
December 10, 2021
A focused machine-learning approach to inducing binding site models with QuanSA is shown to be complementary and synergistic with predictions from FEP+ across 17 targets. The paper reports significant reductions in prediction errors and consistent improvements in compound rank-ordering by combining the results of QuanSA and FEP+. The introductory paper offers additional details about this novel multiple-instance machine-learning method for binding affinity and pose prediction.