ArticleJournal of cheminformatics2023
Integrating synthetic accessibility with AI-based generative drug design.
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 22 papers.
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Who cites it
22 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
- Computational Modeling and MM-PBSA-Based Molecular Dynamics Investigation of Unexplored tRNA Isoleucine Lysidine Synthetase from Mycobacterium tuberculosis with Hyperbranched Amoxicilloyl-Poly-L-Lysine.Journal of molecular modeling · 2026Article
- Integrated structure-based and fragment-guided design of isonicotinic acid derivatives as potential KDM4A inhibitors: an in silico study.Journal of computer-aided molecular design · 2026Article
- Artificial intelligence for precision oncology from phenotyping and drug discovery to clinical translation.Discover oncology · 2026Review
- Developments and challenges in hit progression within fragment-based drug discovery.Nature communications · 2026Review
- Machine Learning-Driven Bioprospecting of Cholinergic Medicinal Plants for Putative Leads Against Butyrylcholinesterase Towards Alzheimer's Care.Biochemistry research international · 2026Article
- Scaling Biomedical Text-Mining: Transformers, GenAI, and Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2026Article
- VNFlow: integration of variational autoencoders and normalizing flows for novel molecular design.Journal of cheminformatics · 2025Article
- Article
- In-Silico discovery of novel cephalosporin antibiotic conformers via ligand-based pharmacophore modelling and de novo molecular design.Journal, genetic engineering & biotechnology · 2025Article
- Growing and linking optimizers: synthesis-driven molecule design.Briefings in bioinformatics · 2025Article
- High-Risk Neuroblastoma Stage 4 (NBS4): Developing a Medicinal Chemistry Multi-Target Drug Approach.Molecules (Basel, Switzerland) · 2025Article
- A combined in silico and MD simulation approach to discover novel LpxC inhibitors targeting multiple drug resistant Pseudomonas aeruginosa.Scientific reports · 2025Article
- Generate what you can make: achieving in-house synthesizability with readily available resources in de novo drug design.Journal of cheminformatics · 2025Article
- Syn-MolOpt: a synthesis planning-driven molecular optimization method using data-derived functional reaction templates.Journal of cheminformatics · 2025Article
- Metal Ion Signaling in Biomedicine.Chemical reviews · 2025Review
- Analog Accessibility Score (AAscore) for Rational Compound Selection.Journal of chemical information and modeling · 2024Article
- Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review.ACS synthetic biology · 2024Article
- Phytochemical on-line screening andRSC advances · 2024Article
- AiZynthFinder 4.0: developments based on learnings from 3 years of industrial application.Journal of cheminformatics · 2024Article
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Authors and funding
8 authors.
Funding
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Abstract
Generative models are frequently used for de novo design in drug discovery projects to propose new molecules. However, the question of whether or not the generated molecules can be synthesized is not systematically taken into account during generation, even though being able to synthesize the generated molecules is a fundamental requirement for such methods to be useful in practice. Methods have been developed to estimate molecule "synthesizability", but, so far, there is no consensus on whether or not a molecule is synthesizable. In this paper we introduce the Retro-Score (RScore), which computes a synthetic accessibility score of molecules by performing a full retrosynthetic analysis through our data-driven synthetic planning software Spaya, and its dedicated API: Spaya-API (https://spaya.ai). We start by comparing several synthetic accessibility scores to a binary "chemist score" as estimated by chemists on a bench of generated molecules, as a first experimental validation that the RScore is a reliable synthetic accessibility score. We then describe a pipeline to generate molecules that validate a list of targets while still being easy to synthesize. We further this idea by performing experiments comparing molecular generator outputs across a range of constraints and conditions. We show that the RScore can be learned by a Neural Network, which leads to a new score: RSPred. We demonstrate that using the RScore or RSPred as a constraint during molecular generation enables our molecular generators to produce more synthesizable solutions, with higher diversity. The open-source Python code containing all the scores and the experiments can be found on ( https://github.com/iktos/generation-under-synthetic-constraint ).
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