Evaluation of AutoML Frameworks for Computational ADMET Screening in Drug Discovery & Development

The goal of this paper is to demonstrate the power of AutoML frameworks for computational ADMET screening in drug discovery and development. For this purpose, we evaluated i) five AutoML frameworks to automate ML tasks using two ADMET datasets, drug induced liver injury (DILI) and hERG inhibition. ii) The performance of manually generated ML and AutoML models, to assess the utility of AutoML models for real world applications. iii) The advantages and disadvantages based on the speed of optimization, robustness, and application domain of the AutoML models. It is observed that i) the performance of manual and AutoML models is found to be very close, in both positive and negative classes. ii) Among the five AutoML frameworks, AutoGluon is found to be the best, in terms of time, robustness, performance etc. iii) However, for easy adoption of AutoML frameworks for real-world applications, one may require a) the selection of high-quality datasets based on scientific context and b) the use of appropriate feature reduction and selection techniques, currently unavailable in many AutoML frameworks.