ArticleJournal of cheminformatics2023
DeepSA: a deep-learning driven predictor of compound synthesis accessibility.
Article in Journal of cheminformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Identification of GPI-Anchored Wall Transfer Protein 1 Modulators for Fungal Infections Through Generative AI and Physics-Based Approaches.International journal of molecular sciences · 2026Article
- Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Unlocking the potential of marine natural product fragments for rational anticancer drug design: a computational approach.Scientific reports · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- In Silico Discovery of ABZI Nitrogen Heterocycle STING Agonists via 3D-QSAR, Molecular Dynamics, and AI-Based Synthesis Prediction.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Article
- RetroScore: graph edit distance-guided retrosynthesis for accessibility scoring with route metrics.Journal of cheminformatics · 2025Article
- Computational Evaluation of Potential c-Abl Kinase Inhibitors for Parkinson's Disease: QSAR, Docking, Bioisosteric Replacement, ADMET, and MD Simulations.Applied biochemistry and biotechnology · 2025Article
- SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models.ACS central science · 2025Article
- Deep Generative AI for Multi-Target Therapeutic Design: Toward Self-Improving Drug Discovery Framework.International journal of molecular sciences · 2025Review
- SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models.ArXiv · 2025Article
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- MolPrice: assessing synthetic accessibility of molecules based on market value.Journal of cheminformatics · 2025Article
- Recent advances in molecular representation methods and their applications in scaffold hopping.npj drug discovery · 2025Review
- Overcoming Clusterin-Induced Chemoresistance in Cancer: A Computational Study Using a Fragment-Based Drug Discovery Approach.Biology · 2025Article
- Artificial Intelligence in Natural Product Drug Discovery: Current Applications and Future Perspectives.Journal of medicinal chemistry · 2025Review
- Molecular optimization using a conditional transformer for reaction-aware compound exploration with reinforcement learning.Communications chemistry · 2025Article
- Conformational Space Profiling Enhances Generic Molecular Representation for AI-Powered Ligand-Based Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Estimating the synthetic accessibility of molecules with building block and reaction-aware SAScore.Journal of cheminformatics · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
With the continuous development of artificial intelligence technology, more and more computational models for generating new molecules are being developed. However, we are often confronted with the question of whether these compounds are easy or difficult to synthesize, which refers to synthetic accessibility of compounds. In this study, a deep learning based computational model called DeepSA, was proposed to predict the synthesis accessibility of compounds, which provides a useful tool to choose molecules. DeepSA is a chemical language model that was developed by training on a dataset of 3,593,053 molecules using various natural language processing (NLP) algorithms, offering advantages over state-of-the-art methods and having a much higher area under the receiver operating characteristic curve (AUROC), i.e., 89.6%, in discriminating those molecules that are difficult to synthesize. This helps users select less expensive molecules for synthesis, reducing the time and cost required for drug discovery and development. Interestingly, a comparison of DeepSA with a Graph Attention-based method shows that using SMILES alone can also efficiently visualize and extract compound's informative features. DeepSA is available online on the below web server ( https://bailab.siais.shanghaitech.edu.cn/services/deepsa/ ) of our group, and the code is available at https://github.com/Shihang-Wang-58/DeepSA .
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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.