ArticleNature communications2023
Leveraging molecular structure and bioactivity with chemical language models for de novo drug design.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
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Who cites it
76 citing papers in PubMed, 151 citations in OpenAlex.
- Chemical language models for early-stage drug discovery: applications, pitfalls, and future directions.Journal of computer-aided molecular design · 2026Review
- PockLigGPT: Pocket-Sequence-Conditioned Molecular Generation with GPTs and RL.Journal of chemical information and modeling · 2026Article
- MMP2Mol: a matched molecular pairs-based framework for ligand-based de novo drug design.Briefings in bioinformatics · 2026Article
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- Generation of antifungals to combat drug resistance using language models and diffusion models.Journal of advanced research · 2026Article
- Activity-cliff awareness enables robust graph learning for molecular property prediction.Nature communications · 2026Article
- A versatile yeast platform for N-demethylation of therapeutic alkaloids.Journal of biological engineering · 2026Article
- The use of artificial intelligence (AI) in neuropsychiatric drug discovery: current challenges and future directions.Translational psychiatry · 2026Review
- Multi-Property De Novo Drug Design Using Deep Learning-Based Knowledge Distillation and Reinforcement Learning.International journal of molecular sciences · 2026Article
- MT-ConBiFormer-GPT: multi-target molecular generation for low-data drug discovery via a contrastive BiFormer-GPT architecture and curriculum learning with cross-domain generalization.Briefings in bioinformatics · 2026Article
- Steering semi-flexible molecular diffusion model for structure-based drug design with reinforcement learning.Science advances · 2026Article
- Structural optimization of drug molecules with incrementally trained language models.Nature communications · 2026Article
- Design and optimization of novel succinate dehydrogenase inhibitors against agricultural fungi based on transformer model.Molecular diversity · 2026Article
- Artificial intelligence for precision oncology from phenotyping and drug discovery to clinical translation.Discover oncology · 2026Review
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Quantum-machine-assisted drug discovery.npj drug discovery · 2026Review
- Accelerating compound synthesis in drug discovery: the role of digitalisation and automation.RSC medicinal chemistry · 2025Article
- Beyond QSARs: Quantitative Knowledge-Activity Relationships (QKARs) for enhanced drug toxicity prediction.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- AI-enabled language models (LMs) to large language models (LLMs) and multimodal large language models (MLLMs) in drug discovery and development.Journal of advanced research · 2025Review
- Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization.Nature communications · 2025Article
16 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
9 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative chemical language models (CLMs) can be used for de novo molecular structure generation by learning from a textual representation of molecules. Here, we show that hybrid CLMs can additionally leverage the bioactivity information available for the training compounds. To computationally design ligands of phosphoinositide 3-kinase gamma (PI3Kγ), a collection of virtual molecules was created with a generative CLM. This virtual compound library was refined using a CLM-based classifier for bioactivity prediction. This second hybrid CLM was pretrained with patented molecular structures and fine-tuned with known PI3Kγ ligands. Several of the computer-generated molecular designs were commercially available, enabling fast prescreening and preliminary experimental validation. A new PI3Kγ ligand with sub-micromolar activity was identified, highlighting the method's scaffold-hopping potential. Chemical synthesis and biochemical testing of two of the top-ranked de novo designed molecules and their derivatives corroborated the model's ability to generate PI3Kγ ligands with medium to low nanomolar activity for hit-to-lead expansion. The most potent compounds led to pronounced inhibition of PI3K-dependent Akt phosphorylation in a medulloblastoma cell model, demonstrating efficacy of PI3Kγ ligands in PI3K/Akt pathway repression in human tumor cells. The results positively advocate hybrid CLMs for virtual compound screening and activity-focused molecular design.
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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.