ArticleJournal of chemical information and modeling2020
Data Set Augmentation Allows Deep Learning-Based Virtual Screening to Better Generalize to Unseen Target Classes and Highlight Important Binding Interactions.
Article in Journal of chemical information and modeling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
23 citing papers in PubMed.
- Incorporating targeted protein structure in deep learning methods for molecule generation in computational drug design.Chemical science · 2025Review
- Spatio-temporal learning from molecular dynamics simulations for protein-ligand binding affinity prediction.Bioinformatics (Oxford, England) · 2025Article
- Beyond Tanimoto: a learned bioactivity similarity index enhances ligand discovery.Frontiers in bioinformatics · 2025Article
- Developing Generalizable Scoring Functions for Molecular Docking: Challenges and Perspectives.Current medicinal chemistry · 2025Review
- CombiningRSC advances · 2024Article
- A Dataset of apical periodontitis lesions in panoramic radiographs for deep-learning-based classification and detection.Data in brief · 2024Article
- Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning.RSC advances · 2024Article
- A comprehensive review of artificial intelligence for pharmacology research.Frontiers in genetics · 2024Review
- Integrated Molecular Modeling and Machine Learning for Drug Design.Journal of chemical theory and computation · 2023Review
- 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
- A Small Step Toward Generalizability: Training a Machine Learning Scoring Function for Structure-Based Virtual Screening.Journal of chemical information and modeling · 2023Article
- Scoring Functions for Protein-Ligand Binding Affinity Prediction using Structure-Based Deep Learning: A Review.Frontiers in bioinformatics · 2022Article
- Predicting target-ligand interactions with graph convolutional networks for interpretable pharmaceutical discovery.Scientific reports · 2022Article
- Galaxy workflows for fragment-based virtual screening: a case study on the SARS-CoV-2 main protease.Journal of cheminformatics · 2022Article
- PIGNet: a physics-informed deep learning model toward generalized drug-target interaction predictions.Chemical science · 2022Article
- AtomNet PoseRanker: Enriching Ligand Pose Quality for Dynamic Proteins in Virtual High-Throughput Screens.Journal of chemical information and modeling · 2022Article
- DLAB: deep learning methods for structure-based virtual screening of antibodies.Bioinformatics (Oxford, England) · 2022Article
- Deep Learning in Structure-Based Drug Design.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases.Methods in molecular biology (Clifton, N.J.) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Current deep learning methods for structure-based virtual screening take the structures of both the protein and the ligand as input but make little or no use of the protein structure when predicting ligand binding. Here, we show how a relatively simple method of data set augmentation forces such deep learning methods to take into account information from the protein. Models trained in this way are more generalizable (make better predictions on protein/ligand complexes from a different distribution to the training data). They also assign more meaningful importance to the protein and ligand atoms involved in binding. Overall, our results show that data set augmentation can help deep learning-based virtual screening to learn physical interactions rather than data set biases.
Indexed as
Identifiers
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.