ArticleComputational biology and chemistry2022
Machine learning prediction of 3CL
Article in Computational biology and chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- A Mini Review on Metal Complexes as Potential Anti-SARS-CoV-2 Agents: Insights from Molecular Docking Studies.Mini reviews in medicinal chemistry · 2026Review
- Exploring the inhibition mechanisms of momordin Ic on S. aureus serine/threonine phosphatase (Stp1) using theoretical and experimental approaches.Scientific reports · 2025Article
- Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR.Nature reviews. Drug discovery · 2024Review
- Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation.Journal of computer-aided molecular design · 2023Article
- Evaluation of Mutual Information and Feature Selection for SARS-CoV-2 Respiratory Infection.Bioengineering (Basel, Switzerland) · 2023Article
- Molecular docking and machine learning affinity prediction of compounds identified upon softwood bark extraction to the main protease of the SARS-CoV-2 virus.Biophysical chemistry · 2022Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular docking results of two training sets containing 866 and 8,696 compounds were used to train three different machine learning (ML) approaches. Neural network approaches according to Keras and TensorFlow libraries and the gradient boosted decision trees approach of XGBoost were used with DScribe's Smooth Overlap of Atomic Positions molecular descriptors. In addition, neural networks using the SchNetPack library and descriptors were used. The ML performance was tested on three different sets, including compounds for future organic synthesis. The final evaluation of the ML predicted docking scores was based on the ZINC in vivo set, from which 1,200 compounds were randomly selected with respect to their size. The results obtained showed a consistent ML prediction capability of docking scores, and even though compounds with more than 60 atoms were found slightly overestimated they remain valid for a subsequent evaluation of their drug repurposing suitability.
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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.