ReviewJournal of chemical information and modeling2023
Open-Source Machine Learning in Computational Chemistry.
Review in Journal of chemical information and modeling, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Challenges and Vision for Standardization of Biopolymer Data Sets for Machine Learning.Biomacromolecules · 2026Review
- Democratizing Artificial Intelligence in Toxicology: Real-World Applications and Automated Computational Workflows.Chemical research in toxicology · 2026Review
- Machine-Learned Extrapolation of Quantum Mechanical Energies in Implicit Solvent from Short to Long Oligopeptides.Journal of chemical information and modeling · 2026Article
- Python-based high-throughput extraction of void information, solvent accessible volume and adsorbate molecules from MOF for adsorption-separation applications.Journal of computer-aided molecular design · 2026Article
- Reindeer: a protein-ligand feature generator software for machine learning algorithms.BMC bioinformatics · 2026Article
- Why Computational Photochemistry Is Challenging and Will Probably Remain So: A Quantum Chemist's Perspective.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AI-driven drug-target interaction prediction: current progress, challenges, and future roadmap for precision medicine.Journal of computer-aided molecular design · 2026Review
- Article
- MDGraphEmb: a toolkit for graph embedding and classification of protein conformational ensembles.Bioinformatics (Oxford, England) · 2025Article
- Enhancing diagnostic precision for thyroid C-TIRADS category 4 nodules: a hybrid deep learning and machine learning model integrating grayscale and elastographic ultrasound features.Quantitative imaging in medicine and surgery · 2025Article
- How to crack a SMILES: automatic crosschecked chemical structure resolution across multiple services using MoleculeResolver.Journal of cheminformatics · 2025Article
- Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach.Molecules (Basel, Switzerland) · 2025Article
- The Biology and Biochemistry of Kynurenic Acid, a Potential Nutraceutical with Multiple Biological Effects.International journal of molecular sciences · 2024Review
- Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow.Journal of cheminformatics · 2024Article
- Machine Learning Prediction of Quantum Yields and Wavelengths of Aggregation-Induced Emission Molecules.Materials (Basel, Switzerland) · 2024Article
- RBPs: an RNA editor's choice.Frontiers in molecular biosciences · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The field of computational chemistry has seen a significant increase in the integration of machine learning concepts and algorithms. In this Perspective, we surveyed 179 open-source software projects, with corresponding peer-reviewed papers published within the last 5 years, to better understand the topics within the field being investigated by machine learning approaches. For each project, we provide a short description, the link to the code, the accompanying license type, and whether the training data and resulting models are made publicly available. Based on those deposited in GitHub repositories, the most popular employed Python libraries are identified. We hope that this survey will serve as a resource to learn about machine learning or specific architectures thereof by identifying accessible codes with accompanying papers on a topic basis. To this end, we also include computational chemistry open-source software for generating training data and fundamental Python libraries for machine learning. Based on our observations and considering the three pillars of collaborative machine learning work, open data, open source (code), and open models, we provide some suggestions to the community.
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.