  Domain-aware machine learning (ML) models have been increasingly adopted for
accelerating small molecule therapeutic design in the recent years. These
models have been enabled by significant advancement in state-of-the-art
artificial intelligence (AI) and computing infrastructures. Several ML
architectures are pre-dominantly and independently used either for predicting
the properties of small molecules, or for generating lead therapeutic
candidates. Synergetically using these individual components along with robust
representation and data generation techniques autonomously in closed loops
holds enormous promise for accelerated drug design which is a time consuming
and expensive task otherwise. In this perspective, we present the most recent
breakthrough achieved by each of the components, and how such autonomous AI and
ML workflow can be realized to radically accelerate the hit identification and
lead optimization. Taken together, this could significantly shorten the
timeline for end-to-end antiviral discovery and optimization times to weeks
upon the arrival of a novel zoonotic transmission event. Our perspective serves
as a guide for researchers to practice autonomous molecular design in
therapeutic discovery.
