Drug discovery is becoming slower and more expensive. Eroom’s law –Moore’s Law in reverse – is that the cost of Research and Development (R&D) of all new drugs approved has risen exponentially over the last 60 years. One of the early steps in drug discovery is predicting the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of candidate drug molecules. ADMET prediction is essential for understanding how drugs are absorbed, distributed, metabolized, and eliminated within the body, which is crucial for determining the pharmacokinetics and safety of potential drug candidates.
Machine/Deep Learning, Graph Neural Networks in particular, have become indispensable to predicting these properties. However, the databases containing the chemical structure of compounds and their corresponding ADMET properties are typically proprietary and a closely guarded secret. Federated Machine learning has shown promising results due to its ability to address challenges related to privacy, data decentralization, and collaboration across multiple institutions to realize more accurate and generalizable models.