Upcoming Events

This online talk will be given by Lance Westerhoff from QuantumBio Inc on Thursday 23 January 2025 at 4pm UK time.

Registration is free, but required - link here.

Title - "Advancing CADD and AI/ML Campaigns through X-ray/Cryo-EM Density-Driven Refinement, Tautomer Analysis, and Ligand Sampling"

Abstract - Accurate protein:ligand structure determination is essential for structure-based drug discovery (SBDD), computer-aided drug design (CADD), and free energy methods (e.g. MovableType, FEP, etcetera). Precise modeling of protein:ligand complexes, including protonation states and explicit solvent effects, underpins key workflows such as docking, thermodynamic calculations, lead optimization, and AI/ML predictions. Traditional X-ray and Cryo-EM refinement methods, however, rely on geometric restraints that often overlook critical interactions, such as hydrogen bonding, polarization, and charge transfer. These limitations result in structural inaccuracies that computational chemists must address with post-hoc molecular mechanics (MM) or quantum mechanics (QM) corrections. QuantumBio has integrated the DivCon semiempirical quantum mechanics (SE-QM) engine into crystallographic refinement workflows to enable automated, density-driven structure preparation (protonation), completion (gap and truncation sampling), density search and ligand placement, and refinement. This approach significantly improves agreement with experimental data while delivering chemically accurate models. It reduces ligand strain, elucidates protein:ligand interactions, and resolves long-standing challenges in structural biology and AI/ML training data development. Beyond refinement, these methods accurately determine critical biochemical features, including tautomeric and protomeric states, chiral centers, rotamer conformations, and solvation effects. With XModeScore, SE-QM refinement identifies protonation states and stereoisomers, even at resolutions where experimental determination is challenging.

This talk will examine how density-driven refinement, tautomer analysis, and ligand sampling enhance SBDD and CADD. By bridging experimental and computational approaches, these methods deliver structurally precise and chemically robust models, advancing AI/ML predictive capabilities and driving more effective drug discovery campaigns.