ArticleNature communications2024
ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning.
Article in Nature communications, 2024. 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.
- A Site-Aware Representation Learning Framework For Unified Molecular Interaction Modeling and Generative Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Design of Synthesizable PROTACs through Synthesis Constrained Generative Model and Reinforcement Learning.JACS Au · 2026Article
- Deep generative models for 3D structure-based drug design and molecular optimisation: a comprehensive survey.Journal of computer-aided molecular design · 2026Review
- SyntheMol-RL: a flexible reinforcement learning framework for designing easily synthesizable antibiotics.Molecular systems biology · 2026Article
- Ultrafast Formation of Aza-Quinone Methides from Suitably SubstitutedJournal of the American Chemical Society · 2026Article
- Experimental and Mechanistic Validation of PARP1pred for Identifying Potent Leads.Computational and structural biotechnology journal · 2026Article
- RetroScore: graph edit distance-guided retrosynthesis for accessibility scoring with route metrics.Journal of cheminformatics · 2025Article
- SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models.ACS central science · 2025Article
- Structure-Based Generation of 3D Small-Molecule Drugs: Are We There Yet?Journal of medicinal chemistry · 2025Article
- SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models.ArXiv · 2025Article
- Accurate structure-activity relationship prediction of antioxidant peptides using a multimodal deep learning framework.Journal of cheminformatics · 2025Article
- Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.International journal of molecular sciences · 2025Review
- Generative AI for navigating synthesizable chemical space.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Designing high-affinity 3D drug molecules via geometric spatial perception diffusion model.Briefings in bioinformatics · 2025Article
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
- From Micro to Marvel: Unleashing the Full Potential of Click Chemistry with Micromachine Integration.Micromachines · 2025Review
- Bridging chemical space and biological efficacy: advances and challenges in applying generative models in structural modification of natural products.Natural products and bioprospecting · 2025Review
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Fatigue life predictor: predicting fatigue life of metallic material using LSTM with a contextual attention model.RSC advances · 2025Article
- Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation.ArXiv · 2025Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
Despite the significant potential of generative models, low synthesizability of many generated molecules limits their real-world applications. In response to this issue, we develop ClickGen, a deep learning model that utilizes modular reactions like click chemistry to assemble molecules and incorporates reinforcement learning along with inpainting technique to ensure that the proposed molecules display high diversity, novelty and strong binding tendency. ClickGen demonstrates superior performance over the other reaction-based generative models in terms of novelty, synthesizability, and docking conformation similarity for existing binders targeting the three proteins. We then proceeded to conduct wet-lab validation on the ClickGen's proposed molecules for poly adenosine diphosphate-ribose polymerase 1. Due to the guaranteed high synthesizability and model-generated synthetic routes for reference, we successfully produced and tested the bioactivity of these novel compounds in just 20 days, much faster than typically expected time frame when handling sufficiently novel molecules. In bioactivity assays, two lead compounds demonstrated superior anti-proliferative efficacy against cancer cell lines, low toxicity, and nanomolar-level inhibitory activity to PARP1. We demonstrate that ClickGen and related models may represent a new paradigm in molecular generation, bringing AI-driven, automated experimentation and closed-loop molecular design closer to realization.
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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.