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Recent 3D structure-based generative methods explicitly model pocket-ligand geometry, but this does not always translate into chemically realistic or practically usable candidate molecules. Molecular language models provide a complementary sequence-based alternative. However, it remains unclear whether sequence-based pocket information can effectively guide ligand generation, whether multistage training improves pocket-specific generation, and whether docking-guided reinforcement learning can be integrated into a practical generation pipeline. We introduce PockLigGPT, a GPT-based framework for pocket-sequence-conditioned molecular generation. Rather than producing fixed 3D coordinates, PockLigGPT formulates ligand design as a sequence-generation problem conditioned on the amino acid composition of the protein pocket. The model is trained in four stages: large-scale chemical pretraining based on ZINC20; bioactivity-oriented adaptation based on ChEMBL; pocket-sequence-conditioned fine-tuning using binding-pocket amino acid sequences paired with ligands; and, finally, pocket-specific docking-guided reinforcement learning using AutoDock Vina-based rewards. PockLigGPT achieves competitive docking-oriented performance under a standardized evaluation protocol while maintaining chemical plausibility, favorable physicochemical profiles, and Lipinski-based drug-likeness. Docking studies on Alzheimer\u2019s disease-associated targets and token-level analyses further support the utility of PockLigGPT for de novo drug design.<\/jats:p>","DOI":"10.1021\/acs.jcim.6c01980","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:02:23Z","timestamp":1786975343000},"page":"10847-10861","source":"Crossref","is-referenced-by-count":0,"title":["PockLigGPT: Pocket-Sequence-Conditioned\nMolecular\nGeneration with GPTs and RL"],"prefix":"10.1021","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1115-4824","authenticated-orcid":true,"given":"Pablo","family":"Varas Pardo","sequence":"first","affiliation":[{"name":"AItenea Biotech S.L. , ,","place":["Madrid, Spain, 28014"]},{"name":"Instituto de Ciencias Matema\u0301ticas (ICMAT-CSIC) , ,","place":["Madrid, Spain, 28049"]},{"name":"Universidad Auto\u0301noma de Madrid, Escuela de Doctorado , ,","place":["Madrid, Spain, 28049"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9443-578X","authenticated-orcid":true,"given":"Guillermo","family":"Marcos-Ayuso","sequence":"additional","affiliation":[{"name":"AItenea Biotech S.L. , ,","place":["Madrid, Spain, 28014"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eugenia","family":"Ulzurrun","sequence":"additional","affiliation":[{"name":"Centro de Investigaciones Biolo\u0301gicas Margarita Salas (CIB Margarita Salas\u2212CSIC) , ,","place":["Madrid, Spain, 28040"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Ri\u0301os Insua","sequence":"additional","affiliation":[{"name":"Instituto de Ciencias Matema\u0301ticas (ICMAT-CSIC) , ,","place":["Madrid, Spain, 28049"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9948-2665","authenticated-orcid":true,"given":"Nuria E.","family":"Campillo","sequence":"additional","affiliation":[{"name":"Centro de Investigaciones Biolo\u0301gicas Margarita Salas (CIB Margarita Salas\u2212CSIC) , ,","place":["Madrid, Spain, 28040"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"2026091409083478900_cit1","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/s10822-013-9672-4","article-title":"Estimation of the size of drug-like\nchemical space based on GDB-17 data","volume":"27","author":"Polishchuk","year":"2013","journal-title":"J. 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