Talk, Da Dao College, Lecture Hall 302, ShanghaiTech University Library, Shanghai, China
From both theoretical and practical perspectives, this article delves into the importance of interdisciplinary collaboration in meeting the demand for talent in modern society and the many benefits it brings, see here, and here to learn more.
Talk, The 9th iHmuan Forum 2023, Shanghai, China
Phenotypic drug discovery (PDD) screens compounds in cellular models that represent disease-relevant phenotypes, offering a compelling alternative to traditional target-based approaches. Unlike conventional methods, where compounds act on a single predefined target, PDD identifies compounds capable of exerting therapeutic effects through multiple targets and mechanisms. This makes PDD particularly valuable for discovering first-in-class drugs, especially for diseases with poorly understood molecular mechanisms or those lacking validated therapeutic targets. By enabling broader exploration of biological systems and uncovering multi-target drugs (polypharmacology), PDD provides a powerful strategy for tackling complex diseases. In this study, we introduce PhenoScreen, an AI-driven deep learning framework designed to advance PDD by utilizing large-scale compound-phenotype association data. Through contrastive learning, PhenoScreen connects chemical space with cellular morphological profiles, allowing for accurate prediction of compound-induced phenotypic changes. The model was validated across multiple screening tasks and successfully predicted active compounds inducing user-specified phenotypes with varying inhibitory effects in the osteosarcoma phenotypic model. Moreover, PhenoScreen demonstrated strong generalization to other tumor cell types, rhabdomyosarcoma evaluated in this study, suggesting its ability to capture key phenotypic features shared across cancer cells. These results underscore PhenoScreen’s potential to accelerate drug discovery by identifying novel therapeutic pathways and increasing the diversity of viable drug candidates. See here to learn more.
Talk, Shanghai Center of Biomedicine Development, Shanghai, China
We won the first prize in the Shanghai International Computational Biology Challenge! GeminiMol is all you need! See here, and here to learn more.
Talk, World Artificial Intelligence Conference 2023, Shanghai, China; Hangzhou, China
The difficulty of synthesizing new molecules generated by molecule generation models, i.e., the synthetic accessibility of compounds, is a key factor affecting the cost of drug development. We propose a deep learning-based chemical language model called DeepSA, which provides a useful tool for drug developers to select target synthetic molecules in real-world studies.DeepSA has a significant advantage in identifying difficult-to-synthesize molecules, with an AUROC of 89.6%, significantly better than existing methods, and with some interpretability. Meanwhile, the model was provided with only the SMILES feature information of molecules during the training process, reflecting the efficient training strategy of the model.