ArticleInternational journal of molecular sciences2023
PETrans: De Novo Drug Design with Protein-Specific Encoding Based on Transfer Learning.
Article in International journal of molecular sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- ANNalog: generation of MedChem-similar molecules.Journal of cheminformatics · 2026Article
- A Comparative Review of Artificial Intelligence Applications in Small Molecule Versus Peptide Drug Discovery.International journal of molecular sciences · 2026Review
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Pushing the boundaries of few-shot learning for low-data drug discovery with a Bayesian meta-learning hypernetwork framework.Briefings in bioinformatics · 2025Article
- Deep reinforcement learning as an interaction agent to steer fragment-based 3D molecular generation for protein pockets.Briefings in bioinformatics · 2025Article
- AI-DrivenJournal of medicinal chemistry · 2025Article
- A review of large language models and autonomous agents in chemistry.Chemical science · 2025Review
- Exploring the potential of large language model-based chatbots in challenges of ribosome profiling data analysis: a review.Briefings in bioinformatics · 2024Review
- A systematic review of deep learning chemical language models in recent era.Journal of cheminformatics · 2024Review
- Generative AI in drug discovery and development: the next revolution of drug discovery and development would be directed by generative AI.Annals of medicine and surgery (2012) · 2024Article
- Developing an Improved Cycle Architecture for AI-Based Generation of New Structures Aimed at Drug Discovery.Molecules (Basel, Switzerland) · 2024Article
- Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.Molecules (Basel, Switzerland) · 2024Review
- Mol-Zero-GAN: zero-shot adaptation of molecular generative adversarial network for specific protein targets.RSC advances · 2023Article
- Attention is all you need: utilizing attention in AI-enabled drug discovery.Briefings in bioinformatics · 2023Review
- Transformer technology in molecular science.Wiley interdisciplinary reviews. Computational molecular scienceArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Recent years have seen tremendous success in the design of novel drug molecules through deep generative models. Nevertheless, existing methods only generate drug-like molecules, which require additional structural optimization to be developed into actual drugs. In this study, a deep learning method for generating target-specific ligands was proposed. This method is useful when the dataset for target-specific ligands is limited. Deep learning methods can extract and learn features (representations) in a data-driven way with little or no human participation. Generative pretraining (GPT) was used to extract the contextual features of the molecule. Three different protein-encoding methods were used to extract the physicochemical properties and amino acid information of the target protein. Protein-encoding and molecular sequence information are combined to guide molecule generation. Transfer learning was used to fine-tune the pretrained model to generate molecules with better binding ability to the target protein. The model was validated using three different targets. The docking results show that our model is capable of generating new molecules with higher docking scores for the target proteins.
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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.