With the advent of more powerful hardware and methods, the use of machine learning (ML) methods has seen a significant upsurge in chemistry-related applications recently. Specifically in drug discovery, the prediction of ADMET (absorption, distribution, metabolism, excretion and toxicity) properties is a main target for ML applications. Herein, we present performance metrics for Schrödingers automated ML model building engine, DeepAutoQSAR, on the ADMET subset of the Therapeutic Data Commons (TDC) — a large collection of public data for ML model building and benchmarking. We also compare the performance of DeepAutoQSAR to the performance of two open source projects, namely ChemProp and DeepPurpose.
DeepAutoQSAR is among the top-performing methods in 20 of the 22 investigated cases, clearly outperforming the other methods in 9 of those. For the other 11 cases, at least one of the other tested methods performs similarly. We believe that continuous development and further improvement of DeepAutoQSAR, in accuracy, robustness to chemical data shift and label efficiency will enable faster and more cost-effective means of drug discovery, ultimately leading to the introduction of novel therapeutics.
It is widely recognized that the ADMET (absorption, distribution, metabolism, excretion and toxicity) profile of novel molecules plays a key role in the successful development of new drugs. This is reinforced by the amount of time and effort spent both in academia and the pharmaceutical industry to develop reliable models to measure and predict numerous related endpoints1. Due to the potentially catastrophic impact of an unfavorable ADMET profile in the later stages of drug development, a common goal is to identify potential issues as early as possible.
With the rise of ultra-large on-demand libraries and DNA encoded libraries (for example Enamine REAL Space or WuXi LabNetwork), early identification of liabilities requires methods that are computationally fast, cheap, and accurate enough to evaluate hundreds of millions of compounds without discarding potentially good candidates. This obviously precludes the use of experimental in vivo or even in vitro methods. Modern machine learning (ML) approaches, often coined artificial intelligence (AI), can easily process millions of molecules on short timescales and low computational costs with acceptable accuracy.
In contrast to physics-based in silico methods, ML/AI methods require high fidelity data to be trained to predict a given endpoint. High-quality training data is often unavailable; data need to be clean and well-curated, and datasets in chemistry applications are often smaller than those used in other domains like ML on images or text. These strict data requirements can limit the application of more complex ML/AI approaches since there is often insufficient amounts of training data to fit complex and accurate models.
However, recognizing the importance of profiling ADMET properties over the past decades, large pharmaceutical companies have generated a wealth of data which is often unfortunately non-public and exclusively applied for internal programs. Public data is rarer, but there are efforts to collect and aggregate public data 2 and also to share non-public data in smart ways to improve existing models while retaining data confidentiality 3.
The successes of deep learning (DL) approaches have led to a renaissance of ML/AI in chemistry applications, with a large number of both open-source and commercial software to pick from when targeting ADMET endpoints. While open-source software oftentimes can profit from faster development cycles and thus implements new scientific insights more quickly, application is often limited to domain experts. On the other hand, commercial software has the benefits of structured quality assurance (QA), documentation and support, and comes coupled with comprehensive user interfaces which significantly lower the barrier to entry for non-experts.
In this paper, we will take a closer look at the performance of two of the more popular open-source packages, ChemProp and DeepPurpose, and Schrödinger’s ML/AI package DeepAutoQSAR, demonstrating their comparative performance on a recently published set of benchmarks.