인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Deep learning models for drug discovery offer broader chemical space exploration and generate more novel molecules compared to traditional methods. Deep learning models currently used in drug design can be divided into two main categories: those based on ligands and those based on protein pockets. However, there is a lack of models that integrate both protein pocket sequence and structural information for molecular generation. This study proposes HybTrans, a molecular generation and optimization model that integrates protein pocket sequence and structural information. The model includes a sequence encoding module, a structure encoding module, a small molecule Self-Referencing Embedded Strings (SELFIES) representation module, a fusion decoding module, and a reinforcement learning module. The evaluation results demonstrate that the molecules produced by HybTrans have an average diversity of 0.815 and a docking affinity score of -7.828 kcal/mol (Vina), surpassing the performance of similar models. Additionally, the generated molecules exhibit high drug-likeness (QED), synthesizability (SA), and Lipinski’s rule of five scores (Lipinski). Ablation studies demonstrate the importance of key modules such as the fusion decoding module and the SELFIES representation module. A case study on the p21-activated kinase (PDB ID: 5i0b) binding pocket shows that HybTrans is capable of capturing the interactions between protein pockets and drug molecules, generating small molecules with high target affinity and excellent molecular properties.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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