ArticleScientific reports2025
Screening of multi deep learning-based de novo molecular generation models and their application for specific target molecular generation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- PockLigGPT: Pocket-Sequence-Conditioned Molecular Generation with GPTs and RL.Journal of chemical information and modeling · 2026Article
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- Hybrid artificial intelligence and quantum annealing as an optimization layer in drug discovery.Patterns (New York, N.Y.) · 2026Review
- Artificial intelligence in respiratory medicine: From diagnosis to treatment and future directions.Chinese medical journal pulmonary and critical care medicine · 2026Review
- VeGA-RX and VeGA-SCX: Controllable SMARTS-Guided Generative Transformers for Precision-DrivenJournal of chemical information and modeling · 2026Article
- A Reinforcement Learning-Guided Genetic Algorithm Integrating Medicinal Chemistry-Inspired Molecular Transformations.Journal of chemical information and modeling · 2026Article
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- Artificial intelligence in non-small cell lung cancer: transforming diagnosis, treatment, and prognostic evaluation.Frontiers in medicine · 2026Review
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- VeGA: A Versatile Generative Architecture for Bioactive Molecules across Multiple Therapeutic Targets.Journal of chemical information and modeling · 2025Article
- Advancing ADMET prediction through multiscale fragment-aware pretraining with MSformer-ADMET.Briefings in bioinformatics · 2025Article
- Current AI technologies in cancer diagnostics and treatment.Molecular cancer · 2025Review
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Authors and funding
4 authors.
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Abstract
Traditional virtual screening methods need to explore expanse and vast chemical spaces and need to be based on existing chemical libraries. With the development of deep learning techniques for the de novo generation of molecules, also known as inverse molecular design, the increasingly widespread application of various types of deep learning algorithms has led to revolutionary changes in de novo molecular generation research. In particular, the emergence of a novel natural language processing (NLP) architecture called the transformer has improved the state-of-the-art performance of existing AI technologies. In this study, we modified one top-performing molecular generation model on the basis of the generative pretraining transformer (GPT) architecture in three directions. Moreover, we propose an integrated end-to-end neural network learning framework based on one complete encoder-decoder architecture transformer model: Transfer Text-to-Text Transformer (T5), by learning the embedding vector representation space of conditional molecular properties to encode and guide the vector representation of SMILES sequences, resulting in the output of the final decoder block with a softmax output (maximum likelihood objective). Moreover, we evaluated the performance of these NLP-based generation models and another new model architecture based on a selective state space and selected the best approach jointing a transfer learning strategy for de novo drug discovery to target L858R/T790M/C797S-mutant EGFR in non-small cell lung cancer.
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