Skip to content
Log in

Overcoming the accuracy–generalization tradeoff in docking and scoring for prospective virtual screening

Garik Petrosyan Vahagn Altunyan Tsolak Ghukasyan Tigran M. Abramyan G. Arakelov Aram Davtyan Tigran Aghajanyan Iskander Nakipov G. Navasardyan Arman Fahradyan Hazarapet Tunanyan Arman Simonyan Pawel L Janczyk Dasmanthie De Silva Hayk Saribekyan Lev Tsidilkovski Vahram Arakelov Narek Ginoyan Hasmik Mnatsakanyan Max Ratnikov Khachik Smbatyan Ashot Papoyan Garegin A. Papoian
Aug 2026 · bioRxiv · 0 citations
Biology
DOI Semantic Scholar
View source

Similar papers

Jul 2026

Real-World Assessment of Machine-Learned Docking Using Bioassay-Derived Benchmarks.

This work systematically evaluates the performance of a popular ML-based docking method, DiffDock-Pocket, on high-throughput screening (HTS) data sets derived from the PubChem BioAssay database, a premier source of bioactivity data.

Furyal Ahmed, Matthew B. Soellner, Charles L. Brooks · 0 citations
Open access Jul 2026

Modeling the Sensitivity of Large-Scale Virtual Screening to Scoring Function Accuracy, Artifacts, and Library Composition

A quantitative framework for understanding how docking performance responds to methodological improvements has been lacking is developed by modeling large-scale experiments from three previously published docking campaigns, providing an objective basis for benchmarking and comparing virtual screening approaches.

Laust Moesgaard, Brian K. Shoichet, Olivier Mailhot · 1 citation
Review Open access Jul 2026

Topological deep learning for drug–target interaction, virtual screening, and docking scoring: a practical, benchmark-driven review

A decision-oriented taxonomy and a benchmark-driven evaluation playbook that specifies minimum standards for splits, metrics, baselines, and ablations to isolate the topological contribution are presented.

Beatriz Suay-García, Antonio Falcó · 0 citations
Preprint Aug 2026

PETA:Parameter-Efficient Test-Time Adaptation for Virtual Screening

This work forms the specialization of pretrained virtual screening models to individual pockets as a test-time adaptation problem and proposes PETA, a parameter-efficient framework that directly adapts pretrained model at test time and outperforms both pretrained and fully retrained baselines while updating only the LayerNorm parameters.

Jia-Qi Lin, Yinghua Yao, Changran Wang et al. · 0 citations
Review Open access Jul 2026

BoltzMol-1: Towards Reliable Virtual Screening for Fast and Cost-Effective Hit Discovery

This work establishes a practical framework for low-throughput, cost-constrained discovery campaigns capable of delivering chemically tractable binders with favorable property profiles, and introduces a suite of ADMET models for kinetic solubility, lipophilicity, and Caco-2 permeability to improve developability at the point of selection.

Noah Getz, Geoffrey Smith, Avene Colgan et al. · 0 citations
Aug 2026

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.

Jaeoh Shin, K. Joo, Jejoong Yoo · 0 citations