Few-Shot Prediction of Drug-Target Binding Affinities for Small Molecule Drug Discovery, Upcoming Webinar Hosted by Xtalks


Accurate prediction of the binding behavior between the target protein and the compounds is crucial for discovering the candidate molecule with decent potency and selectivity.

In the early stage of small molecule drug discovery, accurate prediction of the binding behavior between the target protein and the compounds is crucial for discovering the candidate molecule with decent potency and selectivity. In practical situations, drug-target binding affinity (DTA) prediction becomes difficult because of the lack of a precise protein structure model or known ligands. Data-driven methods are actively being used to conquer the problem. However, the available data is usually insufficient to build a model with generalization capability for the novel targets.

A promising approach to remedy these situations is a few-shot prediction of drug-target bindings, which relies on the small number of the known binding affinities of compounds. A neural process-based model for this purpose (MetaDTA) was recently developed, which does not use the structural information of the proteins. The method is based on meta-learning the known binding affinity values for various protein targets. The prediction accuracy is superior to the state-of-the-art structure-free prediction models, and the model is highly generalizable for unknown targets.

Join this webinar to learn about the proposed meta-learning approach for drug-target binding affinity prediction with the results, limitations and future perspectives.

Join Jiho Yoo, AI Director, Standigm Inc., for the live webinar on Monday, September 26, 2022, at 3pm EDT (12pm PDT).

For more information, or to register for this event, visit Few-Shot Prediction of Drug-Target Binding Affinities for Small Molecule Drug Discovery.

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