Jeffrey Tan

Feb 9, 2024 | biorxiv.org | Xiangru Tang |Andrew Tran |Jeffrey Tan |Mark Gerstein

AbstractThe current paradigm of deep learning models for the joint representation of molecules and text primarily relies on 1D or 2D molecular formats, neglecting significant 3D structural information that offers valuable physical insight. This narrow focus inhibits the models' versatility and adaptability across a wide range of modalities. Conversely, the limited research focusing on explicit 3D representation tends to overlook textual data within the biomedical domain.

Dec 17, 2023 | biorxiv.org | Xiangru Tang |Andrew Tran |Jeffrey Tan |Mark Gerstein

AbstractThe present paradigm of deep learning models for molecular representation relies mostly on 1D or 2D formats, neglecting significant 3D structural information that offers valuable physical insight. This narrow focus inhibits the model's versatility and adaptability across a wide range of modalities. Conversely, the smaller amount of research that focuses on explicit 3D representation tends to overlook textual data within the biomedical domain.

Nov 25, 2023 | biorxiv.org | Andrew Tran |Jeffrey Tan |Mark Gerstein |Xiangru Tang

AbstractThe present paradigm of current deep learning models for molecular representation relies largely on singular 1D or 2D formats, neglecting significant 3D structural information that offers valuable physical insight. This narrow focus inhibits the model's versatility and adaptability across a wide range of modalities.