mlsanityData to 14 Oct 2026

Protein-Ligand Scoring with Convolutional Neural Networks

arXiv 1612.02751 ↗First posted 8 Dec 2016stat.ML cs.LG q-bio.BMcited by 57

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Abstract (arXiv)

Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein-ligand binding and structural data enables the use of deep machine learning techniques for protein-ligand scoring. We describe convolutional neural network (CNN) scoring functions that take as input a comprehensive 3D representation of a protein-ligand interaction. A CNN scoring function automatically learns the key features of protein-ligand interactions that correlate with binding. We train and optimize our CNN scoring functions to discriminate between correct and incorrect binding poses and known binders and non-binders. We find that our CNN scoring function outperforms the AutoDock Vina scoring function when ranking poses both for pose prediction and virtual screening.

Key facts. Cited by 57 papers in the map (data through 14 Oct 2026). First posted on arXiv 8 Dec 2016.

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Citing papers per year: 2020: 1; 2021: 12; 2022: 17; 2023: 14; 2024: 4; 2025: 6; 2026: 3.

57 papers.

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  1. 2022EquiBind: Geometric Deep Learning for Drug Binding Structure Predictioncited by 144
  2. 2021Geometric Deep Learning on Molecular Representationscited by 113
  3. 2022Generating 3D Molecules for Target Protein Bindingcited by 104
  4. 2021Structure-aware Interactive Graph Neural Networks for the Prediction of Protein-Ligand Binding Affinitycited by 60
  5. 2022DiffBP: Generative Diffusion of 3D Molecules for Target Protein Bindingcited by 58
  6. 2022Multi-Scale Representation Learning on Proteinscited by 52
  7. 20233D molecule generation by denoising voxel gridscited by 40
  8. 2021DOCKSTRING: easy molecular docking yields better benchmarks for ligand designcited by 35
  9. 2022E3Bind: An End-to-End Equivariant Network for Protein-Ligand Dockingcited by 35
  10. 2022Contrastive Representation Learning for 3D Protein Structurescited by 30
  11. 2021Artificial Intelligence in Drug Discovery: Applications and Techniquescited by 28
  12. 2023Generalist Equivariant Transformer Towards 3D Molecular Interaction Learningcited by 19
38 more
  1. 2023Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equationcited by 18
  2. 2023FABind: Fast and Accurate Protein-Ligand Bindingcited by 17
  3. 2023EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Predictioncited by 15
  4. 2021Assigning Confidence to Molecular Property Predictioncited by 14
  5. 2023Protein-ligand binding representation learning from fine-grained interactionscited by 14
  6. 2022ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discoverycited by 13
  7. 2022CoSP: Co-supervised pretraining of pocket and ligandcited by 12
  8. 2021Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networkscited by 10
  9. 2024FABind+: Enhancing Molecular Docking through Improved Pocket Prediction and Pose Generationcited by 8
  10. 2025Unified Cross-Scale 3D Generation and Understanding via Autoregressive Modelingcited by 8
  11. 2022Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding Affinitycited by 7
  12. 2022SS-GNN: A Simple-Structured Graph Neural Network for Affinity Predictioncited by 7
  13. 2023Autoregressive fragment-based diffusion for pocket-aware ligand designcited by 7
  14. 2022An efficient graph generative model for navigating ultra-large combinatorial synthesis librariescited by 5
  15. 2024S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Searchcited by 5
  16. 2021High-Throughput Virtual Screening of Small Molecule Inhibitors for SARS-CoV-2 Protein Targets with Deep Fusion Modelscited by 3
  17. 2021A critical look at the current train/test split in machine learningcited by 3
  18. 2022DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded Librariescited by 3
  19. 2022Structure-based drug discovery with deep learningcited by 3
  20. 2023Improved prediction of ligand-protein binding affinities by meta-modelingcited by 3
  21. 2020Deep Inverse Reinforcement Learning for Structural Evolution of Small Moleculescited by 2
  22. 2021Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screeningcited by 2
  23. 2021Improved Drug-target Interaction Prediction with Intermolecular Graph Transformercited by 2
  24. 2023Geometric Deep Learning for Structure-Based Drug Design: A Surveycited by 2
  25. 2023FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretationcited by 2
  26. 2024MIN: Multi-channel Interaction Network for Drug-Target Interaction with Protein Distillationcited by 2
  27. 2025Group Ligands Docking to Protein Pocketscited by 2
  28. 2021ResAtom System: Protein and Ligand Affinity Prediction Model Based on Deep Learningcited by 1
  29. 2021A Novel Framework Integrating AI Model and Enzymological Experiments Promotes Identification of SARS-CoV-2 3CL Protease Inhibitors and Activity-based Probecited by 1
  30. 2022Dynamic Molecular Graph-based Implementation for Biophysical Properties Predictioncited by 1
  31. 2023FlexVDW: A machine learning approach to account for protein flexibility in ligand dockingcited by 1
  32. 2023Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transformscited by 1
  33. 2025Beyond Atoms: Evaluating Electron Density Representation for 3D Molecular Learningcited by 1
  34. 2022Enhanced compound-protein binding affinity prediction by representing protein multimodal information via a coevolutionary strategycited by 0
  35. 2022Predicting Protein-Ligand Binding Affinity with Equivariant Line Graph Networkcited by 0
  36. 2022Recent Developments in Structure-Based Virtual Screening Approachescited by 0
  37. 2023Learning Universal and Robust 3D Molecular Representations with Graph Convolutional Networkscited by 0
  38. 2023Target-Free Compound Activity Prediction via Few-Shot Learningcited by 0

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