We apply a range of machine learning algorithms, including but not limited to supervised learning, unsupervised learning, reinforcement learning and deep learning, as well as apply data visualization and interpretation, to provide insights into problems related to data science and artificial intelligence.
Owing to the huge amount of data generated from the past research efforts, AI and ML tools can be applied to each phase of drug development pipeline to identify patterns and thus accelerate the overall process and reduce the risk and expenditure in later clinical trials. Currently, our Binary Star team is heavily involved in the application of AI and machine learning tools for predicting agonist/antagonist ligands for GPCRs. This can help identify novel drugs and predict the potential efficacy of drug candidates.
