How Can QM Methods Be Used to Guide Synthesis Planning in Drug Discovery?

Quantum-Driven High-Throughput Docking: Advances in Molecular Scoring and Drug Discovery.

Recent developments in QM-based molecular docking and HTD are reviewed, focusing on cases where QM is used explicitly in docking workflows.

Perspectives on Hybridizing Quantum Mechanics and Artificial Intelligence for Drug Design.

Quantum Chemistry in Drug Design: Applications, Challenges, and the Need for Speed.

Key QM applications-torsional profiles, spectra prediction, reactivity analysis, and modeling of non-covalent interactions-highlighting their impact and limitations are reviewed, illustrating the trade-off between speed and accuracy.

Systematic Benchmarking of AI-Based Molecular Generation Models for Structure-Based Drug Design

A state-aware functional classifier (SAFC) is developed that integrates molecular dynamics derived receptor ensembles, ensemble docking and protein ligand interaction graphs that provides dynamics-aware functional activity rankings for generated molecules that were partly complementary to docking, drug-likeness and synthetic accessibility scores.

Open-Sourced In Silico Drug Screening.

This chapter describes a structure-based computational approach to perform high-throughput ligand screens of chemical libraries using open-source software programs and illustrates this workflow with the enzymatic molecular target NAD(P)H:quinone oxidoreductase1 (NQO1), which is overexpressed in a number of human solid tumors.

Evaluating BioEmu-Generated Kinase Ensembles Reveals Structure Selection as the Virtual Screening Bottleneck.

It is shown that prospective structure selection, rather than structure generation, represents the primary bottleneck in ensemble-based VS, highlighting an urgent need for novel structural descriptors to identify high-performing conformations.