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Autor:
Cavanagh, Joseph M., Sun, Kunyang, Gritsevskiy, Andrew, Bagni, Dorian, Bannister, Thomas D., Head-Gordon, Teresa
Here we show that a Large Language Model (LLM) can serve as a foundation model for a Chemical Language Model (CLM) which performs at or above the level of CLMs trained solely on chemical SMILES string data. Using supervised fine-tuning (SFT) and dire
Externí odkaz:
http://arxiv.org/abs/2409.02231
Autor:
Li, Jie, Guan, Xingyi, Zhang, Oufan, Sun, Kunyang, Wang, Yingze, Bagni, Dorian, Head-Gordon, Teresa
Many physics-based and machine-learned scoring functions (SFs) used to predict protein-ligand binding free energies have been trained on the PDBBind dataset. However, it is controversial as to whether new SFs are actually improving since the general,
Externí odkaz:
http://arxiv.org/abs/2308.09639
Autor:
Li, Jie, Zhang, Oufan, Wang, Yingze, Sun, Kunyang, Guan, Xingyi, Bagni, Dorian, Haghighatlari, Mojtaba, Kearns, Fiona L., Parks, Conor, Amaro, Rommie E., Head-Gordon, Teresa
The viability of a new drug molecule is a time and resource intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibitor molecules
Externí odkaz:
http://arxiv.org/abs/2110.01806
Akademický článek
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Publikováno v:
ArXiv [ArXiv] 2024 May 03. Date of Electronic Publication: 2024 May 03.
