mlsanityData to 14 Oct 2026

Recent Developments in Structure-Based Virtual Screening Approaches

arXiv 2211.03208 ↗First posted 6 Nov 2022q-bio.BM cs.LG physics.bio-phcited by 0

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Abstract (arXiv)

Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, as well as a low number of new drugs that are approved each year. To solve these problems, new and innovate technologies are needed that make the drug discovery process of small-molecules more time and cost-efficient, and which allow to target previously undruggable target classes such as protein-protein interactions. Structure-based virtual screenings have become a leading contender in this context. In this review, we give an introduction to the foundations of structure-based virtual screenings, and survey their progress in the past few years. We outline key principles, recent success stories, new methods, available software, and promising future research directions. Virtual screenings have an enormous potential for the development of new small-molecule drugs, and are already starting to transform early-stage drug discovery.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 142 references that are in the map. First posted on arXiv 6 Nov 2022.

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References · 14

  1. 2022DiffDock: Diffusion Steps, Twists, and Turns for Molecular Dockingcited by 313
  2. 2022EquiBind: Geometric Deep Learning for Drug Binding Structure Predictioncited by 144
  3. 2022MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Designcited by 66
  4. 2020Accelerating high-throughput virtual screening through molecular pool-based active learningcited by 59
  5. 2016Protein-Ligand Scoring with Convolutional Neural Networkscited by 57
  6. 2018DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networkscited by 52
  7. 2017Development and evaluation of a deep learning model for protein-ligand binding affinity predictioncited by 51
  8. 2018PotentialNet for Molecular Property Predictioncited by 46
  9. 2015AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discoverycited by 38
  10. 2017Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinitycited by 22
  11. 2019DeepAtom: A Framework for Protein-Ligand Binding Affinity Predictioncited by 14
  12. 2019Combining docking pose rank and structure with deep learning improves protein-ligand binding mode predictioncited by 10
2 more
  1. 2018Visualizing Convolutional Neural Network Protein-Ligand Scoringcited by 3
  2. 2017Ligand Pose Optimization with Atomic Grid-Based Convolutional Neural Networkscited by 2

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