ArticleJournal of molecular graphics & modelling2018
Visualizing convolutional neural network protein-ligand scoring.
Article in Journal of molecular graphics & modelling, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- Calculating Enzyme Inhibition with Random Forests.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Peering inside the black box by learning the relevance of many-body functions in neural network potentials.Nature communications · 2025Article
- Quality over quantity: how to get the best results when using docking for repurposing.Frontiers in bioinformatics · 2025Article
- Virtual Screening, Molecular Dynamics, and Mechanism Study of Homeodomain-Interacting Protein Kinase 2 Inhibitor in Renal Fibroblasts.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Deep generative models of protein structure uncover distant relationships across a continuous fold space.Nature communications · 2024Article
- The Impact of Data on Structure-Based Binding Affinity Predictions Using Deep Neural Networks.International journal of molecular sciences · 2023Article
- Exploring the ability of machine learning-based virtual screening models to identify the functional groups responsible for binding.Journal of cheminformatics · 2023Article
- Predicting the target landscape of kinase inhibitors using 3D convolutional neural networks.PLoS computational biology · 2023Article
- Structure prediction in low dimensions: concepts, issues and examples.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
- A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening.Journal of chemical information and modeling · 2023Article
- A Perspective on Explanations of Molecular Prediction Models.Journal of chemical theory and computation · 2023Review
- Layer-Wise Relevance Analysis for Motif Recognition in the Activation Pathway of theInternational journal of molecular sciences · 2023Article
- Beyond sequence: Structure-based machine learning.Computational and structural biotechnology journal · 2023Review
- A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios.Journal of computer-aided molecular design · 2022Article
- Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.Frontiers in bioinformatics · 2022Article
- PlayMolecule Glimpse: Understanding Protein-Ligand Property Predictions with Interpretable Neural Networks.Journal of chemical information and modeling · 2022Article
- Artificial intelligence and inflammatory bowel disease: practicalities and future prospects.Frontline gastroenterology · 2022Article
- Virtual Screening with Gnina 1.0.Molecules (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Protein-ligand scoring is an important step in a structure-based drug design pipeline. Selecting a correct binding pose and predicting the binding affinity of a protein-ligand complex enables effective virtual screening. Machine learning techniques can make use of the increasing amounts of structural data that are becoming publicly available. Convolutional neural network (CNN) scoring functions in particular have shown promise in pose selection and affinity prediction for protein-ligand complexes. Neural networks are known for being difficult to interpret. Understanding the decisions of a particular network can help tune parameters and training data to maximize performance. Visualization of neural networks helps decompose complex scoring functions into pictures that are more easily parsed by humans. Here we present three methods for visualizing how individual protein-ligand complexes are interpreted by 3D convolutional neural networks. We also present a visualization of the convolutional filters and their weights. We describe how the intuition provided by these visualizations aids in network design.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.