VIRTECS: Virtual Screening Of Therapeutic Classes Using Encodings Of Chemical Structures

In recent times, the need for virtual screening of chemical compounds has grown with the advent of computational synthesis and de-novo generation of drugs. The state-of-the-art benchmarks of virtual screening today, incorporate chemical and physiological properties, binding affinities, along with the targets of known chemical compounds. However, benchmarks for the classification of drugs into overarching functional groups based purely on the structure of the compound are yet to be explored. In this paper, we introduce VIRTECS: a tool that leverages the simplified molecular-input line-entry system (SMILES) – a structural representation of a drug – to enable virtual screening of large scale chemical databases, based on the therapeutic classes of drugs. The only input required by the system is the SMILES representation, one that is readily available with most computational generation approaches. The experimental results on multiple datasets demonstrate the potency of structural information in determining the functional groups of chemical compounds. VIRTECS holds enormous potential in yielding insights into various properties of novel molecules when an embedding of the SMILES input is used and paired with an apposite graph algorithm, and tested with known molecules. We present a framework that allows for multiple combinations of the input (SMILES with or without the embedding) and a choice of models and databases that can be tested based on the desired output: insight to the function or potential therapeutic value of a chemical compound.