ChemFixer: Correcting Invalid Molecules to Unlock Previously Unseen Chemical Space

TL;DR

ChemFixer is a transformer-based framework that corrects invalid molecules generated by deep learning models, thereby expanding accessible chemical space and improving drug discovery processes.

Contribution

It introduces ChemFixer, a novel method for correcting invalid molecules using a transformer architecture trained on large-scale molecular pairs.

Findings

Significantly improves molecular validity across generative models.

Preserves chemical and biological distributional properties of molecules.

Enhances drug discovery by expanding chemical space and aiding downstream tasks.

Abstract

Deep learning-based molecular generation models have shown great potential in efficiently exploring vast chemical spaces by generating potential drug candidates with desired properties. However, these models often produce chemically invalid molecules, which limits the usable scope of the learned chemical space and poses significant challenges for practical applications. To address this issue, we propose ChemFixer, a framework designed to correct invalid molecules into valid ones. ChemFixer is built on a transformer architecture, pre-trained using masking techniques, and fine-tuned on a large-scale dataset of valid/invalid molecular pairs that we constructed. Through comprehensive evaluations across diverse generative models, ChemFixer improved molecular validity while effectively preserving the chemical and biological distributional properties of the original outputs. This indicates that ChemFixer can recover molecules that could not be previously generated, thereby expanding the diversity of potential drug candidates. Furthermore, ChemFixer was effectively applied to a drug-target interaction (DTI) prediction task using limited data, improving the validity of generated ligands and discovering promising ligand-protein pairs. These results suggest that ChemFixer is not only effective in data-limited scenarios, but also extensible to a wide range of downstream tasks. Taken together, ChemFixer shows promise as a practical tool for various stages of deep learning-based drug discovery, enhancing molecular validity and expanding accessible chemical space.