Accurate prediction of drug target binding affinity is essential in the early stages of drug discovery. This is an example of finetuning ibm/biomed.omics.bl.sm ted 400 the task. Prediction of binding affinities using pKd, the negative logarithm of the dissociation constant, which reflects the strength of the interaction between a small molecule (drug) and a protein (target). The expected inputs for the model are the amino acid sequence of the target and the SMILES representation of the drug. The benchmark used for fine tuning defined on: https://tdcommons.ai/multi pred tasks/dti/ We also harmonize the values using data.harmonize affinities(mode = 'max affinity') and transforming to log scale. By default, we are using Drug+Target cold split, as provided by tdcommons. Model Summary Developers: IBM Research GitHub Repository: https://github.com/BiomedSciAI/biomed multi alignment Paper: https://arxiv.org/abs/2410.22367 Release Date : Oct 28th, 2024 License: Apache 2.0. Usage Using ibm/biomed.omics.bl.sm.ma ted 458m requires installing https://github.com/BiomedSciAI/biomed multi alignment A simple example for a task already supported by ibm/biomed.omics.bl.sm.ma ted 458m : For more adva…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy