A TOOLBOX OF GENERATIVE MODELS AND DTA PREDICTION FOR IN-SILICO MOLECULAR DESIGN AND DRUG DISCOVERY

Abstract

The typical drug development process involves multiple stages, including target identification, target validation, lead discovery, lead optimizations, ADMET evaluation, and several phases of clinical trials leading to registration [27]. This standard flow usually spans around 17 years, with the chances of successful drug registration being only 1 out of 5000 [26]. To facilitate in-silico studies, various tools like RDKit [28], Open Babel [29], SWISS-MODEL [32], and AutoDock Vina [30] have been developed. However, the decentralized development of many frameworks has given rise to challenges like version conflicts, platform dependencies, complex installations, and scattered knowledge. This makes it difficult and time-consuming for new researchers to get onboarded in the domain, often requiring several weeks or even half a year to study existing frameworks and workflows. My framework, DDBox, aims to address these issues by consolidating the most popular tools into a single platform. By doing so, it not only simplifies in-silico studies but also contributes to knowledge sharing in the in-silico drug design field.