ArticleACS central science2019
Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction.
Article in ACS central science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 305 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.
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Who cites it
305 citing papers in PubMed.
- AI-Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery.Medicinal research reviews · 2026Review
- Bridging Chemical Literature and Molecular Graphs: LLM-Augmented Enantioselectivity Prediction for Iridium-Catalyzed Asymmetric C-H Borylation.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Chemistry-informed Bayesian control and policy distillation for autonomous pH adjustment in diverse chemical systems.Chemical science · 2026Article
- Chemical language models for early-stage drug discovery: applications, pitfalls, and future directions.Journal of computer-aided molecular design · 2026Review
- Artificial intelligence-assisted lead optimization in drug discovery: bridging computational advances and translational challenges.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities.Cancers · 2026Review
- DFRL-Mol: a dual-stage framework of reinforcement learning for multi-scenario molecule optimization.Briefings in bioinformatics · 2026Article
- The impact of reward scalarization and weight scheduling on optimization dynamics in multi-objective molecular design.Journal of cheminformatics · 2026Article
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- Which chemical features are captured by ChemBERTa's attention?Journal of computer-aided molecular design · 2026Article
- Diversity Beats Size Scaling for Chemical Language Models.npj drug discovery · 2026Article
- ConfRetro: a 3D-aware template-free method for enhancing retrosynthesis via molecular conformer information.Bioinformatics (Oxford, England) · 2026Article
- High-throughput solid microsampling through stochastic robotic automation.Communications chemistry · 2026Article
- HELM-BERT: Topology-Aware Representations for Chemically Modified Peptides.Journal of chemical information and modeling · 2026Article
- Artificial intelligence in impurity prediction: current landscape, challenges, and future directions.Journal of computer-aided molecular design · 2026Review
- The use of artificial intelligence in advancing molecular biology in Africa: a narrative review.Molecular genetics and genomics : MGG · 2026Review
- End-to-end molecular structure elucidation from multimodal NMR spectra images using vision transformers.Chemical science · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Evaluating AI-Generated Molecules for Drug Discovery: From Generic Metrics to Translational Readiness.International journal of molecular sciences · 2026Review
245 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Organic synthesis is one of the key stumbling blocks in medicinal chemistry. A necessary yet unsolved step in planning synthesis is solving the forward problem: Given reactants and reagents, predict the products. Similar to other work, we treat reaction prediction as a machine translation problem between simplified molecular-input line-entry system (SMILES) strings (a text-based representation) of reactants, reagents, and the products. We show that a multihead attention Molecular Transformer model outperforms all algorithms in the literature, achieving a top-1 accuracy above 90% on a common benchmark data set. Molecular Transformer makes predictions by inferring the correlations between the presence and absence of chemical motifs in the reactant, reagent, and product present in the data set. Our model requires no handcrafted rules and accurately predicts subtle chemical transformations. Crucially, our model can accurately estimate its own uncertainty, with an uncertainty score that is 89% accurate in terms of classifying whether a prediction is correct. Furthermore, we show that the model is able to handle inputs without a reactant-reagent split and including stereochemistry, which makes our method universally applicable.
Identifiers
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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.