07/10/2020
Traditional drug discovery methods are target-driven, i.e., a known target is used to screen for small
molecules that either interact with it or affect its function in cells.
● These approaches work well for easily druggable targets that have a well-defined structure and
whose interactions inside the cell are understood in detail.
● However, these methods are extremely limited due to the complex nature of cellular
interactions as well as limited knowledge of intricate cellular pathways.
AI can overcome these challenges by identifying novel interactions and inferring functional
importance of different components of a cellular pathway.
● AI utilizes complex algorithms and machine learning to extract meaningful information from a
large dataset, e.g., a dataset of RNA sequencing can be used to identify genes whose
expression correlates with a given cellular condition.
● AI can also be used to identify compounds that could bind to ‘undruggable targets’, i.e.,
proteins whose structures are not defined. Through iterative simulations of interactions of
different compounds with small pieces of a protein, a predictive set of compounds can be
easily identified in a relatively small amount of time.
