ArticleMolecules (Basel, Switzerland)2020
A Deep-Learning Approach toward Rational Molecular Docking Protocol Selection.
Article in Molecules (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic Approach.Molecules (Basel, Switzerland) · 2021Pooled it
- PD-L1 targeting in triple negative breast cancer: in silico and in vitro validation of wasp venom peptide MP-1.Medical oncology (Northwood, London, England) · 2025Article
- Merging Counter-Propagation and Back-Propagation Algorithms: Overcoming the Limitations of Counter-Propagation Neural Network Models.International journal of molecular sciences · 2024Article
- Opportunities and challenges in application of artificial intelligence in pharmacology.Pharmacological reports : PR · 2023Review
- Cheminformatics and artificial intelligence for accelerating agrochemical discovery.Frontiers in chemistry · 2023Review
- Structure-based drug repurposing: Traditional and advanced AI/ML-aided methods.Drug discovery today · 2022Review
- Drug Discovery forInternational journal of molecular sciences · 2021Review
- Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction.International journal of molecular sciences · 2021Review
- Structure-basedComputational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While a plethora of different protein-ligand docking protocols have been developed over the past twenty years, their performances greatly depend on the provided input protein-ligand pair. In this study, we developed a machine-learning model that uses a combination of convolutional and fully connected neural networks for the task of predicting the performance of several popular docking protocols given a protein structure and a small compound. We also rigorously evaluated the performance of our model using a widely available database of protein-ligand complexes and different types of data splits. We further open-source all code related to this study so that potential users can make informed selections on which protocol is best suited for their particular protein-ligand pair.
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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.