ArticleJournal of chemical information and modeling2024
Chemprop: A Machine Learning Package for Chemical Property Prediction.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 247 papers, 1 of them a synthesis that pooled it.
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Who cites it
247 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- AIMNet2-rxn: A machine-learned potential for generalized reaction modeling on a millions-of-pathways scale.Science advances · 2026Article
- COSMO-NET: fast and accurate machine learning surrogates for COSMO-based molecular descriptors.Molecular systems design & engineering · 2026Article
- Conformer-covariance diagnostics for aqueous solubility.Journal of computer-aided molecular design · 2026Article
- Times are changing but order matters: transferable prediction of small-molecule liquid chromatography retention times.Nature methods · 2026Article
- PharmaGNN: a model for odor prediction based on graph neural networks.Journal of the science of food and agriculture · 2026Article
- Multiobjective Fluorescent Molecule Design with a Data-Physics Dual-Driven Generative Framework.JACS Au · 2026Article
- Measuring AI Progress in Drug Discovery: A Reproducible Leaderboard for the Tox21 Challenge.Journal of chemical information and modeling · 2026Article
- Deep learning of tautomer stability from crystallographic proton positions.Chemical science · 2026Article
- Machine Learning-Guided Discovery of Bacterial-Selective Membrane-Active Compounds Reveals Mechanistic Bias in Antibiotic Training Datasets.bioRxiv : the preprint server for biology · 2026Article
- µORScreen: a lightweight consensus modeling framework for µ-opioid receptor ligand prediction and virtual screening.RSC advances · 2026Article
- Targeting BCL-2 through Deep Learning-Based Drug Repurposing: A Multimodal Approach Combining Diffusion-Based Generative Modeling, Neural Relational Inference, and In Vitro Validation.Journal of chemical information and modeling · 2026Article
- Integrating Low-Cost xTB Electronic Priors for Predicting Electronic and Excited-State Properties of Organic Conjugated Molecules.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- Differentiable Thermodynamic Phase-Equilibria for Machine Learning.Journal of chemical information and modeling · 2026Article
- How effective are contrastive learning-based approaches for activity-cliff prediction?bioRxiv : the preprint server for biology · 2026Article
187 more citing papers are in PubMed but not listed here.
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Authors and funding
9 authors.
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Abstract
Deep learning has become a powerful and frequently employed tool for the prediction of molecular properties, thus creating a need for open-source and versatile software solutions that can be operated by nonexperts. Among the current approaches, directed message-passing neural networks (D-MPNNs) have proven to perform well on a variety of property prediction tasks. The software package Chemprop implements the D-MPNN architecture and offers simple, easy, and fast access to machine-learned molecular properties. Compared to its initial version, we present a multitude of new Chemprop functionalities such as the support of multimolecule properties, reactions, atom/bond-level properties, and spectra. Further, we incorporate various uncertainty quantification and calibration methods along with related metrics as well as pretraining and transfer learning workflows, improved hyperparameter optimization, and other customization options concerning loss functions or atom/bond features. We benchmark D-MPNN models trained using Chemprop with the new reaction, atom-level, and spectra functionality on a variety of property prediction data sets, including MoleculeNet and SAMPL, and observe state-of-the-art performance on the prediction of water-octanol partition coefficients, reaction barrier heights, atomic partial charges, and absorption spectra. Chemprop enables out-of-the-box training of D-MPNN models for a variety of problem settings in fast, user-friendly, and open-source software.
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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.