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.

Tools Module: ForceGen methodology

Docking Module: Virtual screening and highly accurate pose prediction

Similarity Module: eSim method for virtual screening and multiple ligand alignment

Affinity Module: Machine learning using QuanSA

What’s New?

JCIM: Improvements in Docking
April 9, 2020

The latest improvements in Surflex-Dock, especially with respect to virtual screening, has been published in a special issue of JCIM. Ensemble docking is shown to produce much better results than single-structure docking on the DUD-E+ benchmark. Ligand-based methods employing eSim are shown to be competitive as well. Hybrid approaches are much better than single-mode approaches.

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.