Hi all,

Our first industry talk of the year is by Lance Westerhoff from QuantumBio Inc 
on Thursday 23 January 2025 at 4pm UK time. Details and free registration at 
https://www.ccpbiosim.ac.uk/quantumbio2025.

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.

Best wishes,
Sarah

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