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Drug discovery is a tedious and time-consuming task involving intensive chemistry to design and facilitate the effective and safe synthesis of compounds at a large scale. AI in drug discovery has remarkable implications for researchers to overcome the limitations concerning the clinical evaluation of drug-like compounds.
AI-based models will therefore accelerate the discovery of drugs more quickly and efficiently.
The discovery and synthesis of novel drugs are time consuming and expensive process due to the problems faced in large-scale chemical synthesis and biological evaluation of drug compounds. In the past decade, Artificial Intelligence (AI) has gained great importance in in-silico drug discovery endeavors.
In that context, we at ABL are working towards addressing the existing dearth of PKM2 modulators as this protein has been identified as a recurring biological target in the past decade controlling tumor metastasis. Currently, we are working on designing an AI-based model to classify small-molecule modulators designed for PKM2 as activators and inhibitors thus alleviating the limitations of experimental studies. Furthermore, the model is being trained to predict the bioactivity of PKM2 activators. This model will be a foundation for future predictions of AC50 and IC50 values of PKM2 based on its descriptors, ultimately providing valuable information for relevant research and applications.
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