ArticleChemical science2021
DeepReac+: deep active learning for quantitative modeling of organic chemical reactions.
Article in Chemical science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- ASCENT: an active transfer learning paradigm for efficient drug-target interaction prediction.Science China. Life sciences · 2026Article
- Artificial intelligence in impurity prediction: current landscape, challenges, and future directions.Journal of computer-aided molecular design · 2026Review
- AI-Driven Design Platforms of Next-Generation Antibody Therapeutics.Topics in current chemistry (Cham) · 2026Review
- Benchmarking molecular representations and machine learning algorithms for asymmetric catalysis: a palladium-catalysed decarboxylative asymmetric allylic alkylation case study.Journal of cheminformatics · 2026Article
- ChemTorch: A Deep Learning Framework for Benchmarking and Developing Chemical Reaction Property Prediction Models.Journal of chemical information and modeling · 2026Article
- Machine Learning-Driven Design of Fluorescent Materials: Principles, Methodologies, and Future Directions.Nanomaterials (Basel, Switzerland) · 2025Review
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Article
- An active representation learning method for reaction yield prediction with small-scale data.Communications chemistry · 2025Article
- Deep active learning with high structural discriminability for molecular mutagenicity prediction.Communications biology · 2024Article
- Finding the most potent compounds using active learning on molecular pairs.Beilstein journal of organic chemistry · 2024Article
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
- A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data.Journal of cheminformatics · 2023Article
- ResNet14Attention network for identifying the titration end-point of potassium dichromate.Heliyon · 2023Article
- Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge.Nature communications · 2023Article
- Active learning for efficient analysis of high-throughput nanopore data.Bioinformatics (Oxford, England) · 2023Article
- Navigating chemical reaction space - application to DNA-encoded chemistry.Chemical science · 2022Article
- Review
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Authors and funding
5 authors.
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Abstract
Various computational methods have been developed for quantitative modeling of organic chemical reactions; however, the lack of universality as well as the requirement of large amounts of experimental data limit their broad applications. Here, we present DeepReac+, an efficient and universal computational framework for prediction of chemical reaction outcomes and identification of optimal reaction conditions based on deep active learning. Under this framework, DeepReac is designed as a graph-neural-network-based model, which directly takes 2D molecular structures as inputs and automatically adapts to different prediction tasks. In addition, carefully-designed active learning strategies are incorporated to substantially reduce the number of necessary experiments for model training. We demonstrate the universality and high efficiency of DeepReac+ by achieving the state-of-the-art results with a minimum of labeled data on three diverse chemical reaction datasets in several scenarios. Collectively, DeepReac+ has great potential and utility in the development of AI-aided chemical synthesis. DeepReac+ is freely accessible at https://github.com/bm2-lab/DeepReac.
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