FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning
This toolkit addresses the need for fair and efficient benchmarking in drug discovery by providing a comprehensive framework for researchers, though it is incremental as it builds on existing MRL methods.
The paper tackles the challenge of benchmarking molecular relational learning (MRL) by introducing FlexMol, a flexible toolkit that supports over 70,000 model architecture combinations and includes preset components like 16 drug encoders and 13 protein sequence encoders to simplify and standardize model development and comparison.
Molecular relational learning (MRL) is crucial for understanding the interaction behaviors between molecular pairs, a critical aspect of drug discovery and development. However, the large feasible model space of MRL poses significant challenges to benchmarking, and existing MRL frameworks face limitations in flexibility and scope. To address these challenges, avoid repetitive coding efforts, and ensure fair comparison of models, we introduce FlexMol, a comprehensive toolkit designed to facilitate the construction and evaluation of diverse model architectures across various datasets and performance metrics. FlexMol offers a robust suite of preset model components, including 16 drug encoders, 13 protein sequence encoders, 9 protein structure encoders, and 7 interaction layers. With its easy-to-use API and flexibility, FlexMol supports the dynamic construction of over 70, 000 distinct combinations of model architectures. Additionally, we provide detailed benchmark results and code examples to demonstrate FlexMol's effectiveness in simplifying and standardizing MRL model development and comparison.