Fingerprint-Enhanced Graph Attention Network (FinGAT) Model for Antibiotic Discovery

Abstract

Artificial Intelligence (AI) techniques are of great potential to fundamentally change antibiotic discovery industries. Efficient and effective molecular featurization is key to all highly accurate learning models for antibiotic discovery. In this paper, we propose a fingerprint-enhanced graph attention network (FinGAT) model by the combination of sequence-based 2D fingerprints and structure-based graph representation. In our feature learning process, sequence information is transformed into a fingerprint vector, and structural information is encoded through a GAT module into another vector. These two vectors are concatenated and input into a multilayer perceptron (MLP) for antibiotic activity classification. Our model is extensively tested and compared with existing models. It has been found that our FinGAT can outperform various state-of-the-art GNN models in antibiotic discovery.

FP-MAP: an extensive library of fingerprint-based molecular activity prediction tools

“…In this study, Avalon and FCFP4 fingerprints are generally seen standout as useful descriptors and may serve as useful starting points for future benchmarking studies. A potential avenue for improvement in prediction performance could be to combine 2D fingerprints with structure-based graph representations ( Choo et al, 2023 ). Alternatively, one may look towards language representations which have recently been shown to yield good results on several classification and regression benchmarks ( Ross et al, 2022 ).…”

Exploring Graph and Digraph Persistence

“…This might have been the case for the problems faced, e.g., in [41] for startling deductions from microglial morphology, in [42,43] for proteinbinding prediction, in [44] for RNA analysis, in [45] for investigating market crashes, and in [46] for genealogical studies. Research such as that presented in [47] on antibiotic discovery did not make use of persistence; we suspect that our new persistence techniques might boost its representation and analysis power. Some of the above-referenced papers modeled their data as hypergraphs, so it could be interesting and useful to extend our methods to this type of structure.…”

Exploring Graph and Digraph Persistence

“…This might have been the case for the problems faced, e.g., in [41] for startling deductions from microglial morphology, in [42,43] for proteinbinding prediction, in [44] for RNA analysis, in [45] for investigating market crashes, and in [46] for genealogical studies. Research such as that presented in [47] on antibiotic discovery did not make use of persistence; we suspect that our new persistence techniques might boost its representation and analysis power. Some of the above-referenced papers modeled their data as hypergraphs, so it could be interesting and useful to extend our methods to this type of structure.…”