A systematic investigation of molecular encoding methods for drug property predictions across neural network and Transformer encoder-based model
Fundamental investigations into how different molecular encoding methods affect molecular property prediction remain relatively limited. In this study, we extensively examined the optimal molecular encoding methods for molecular properties prediction using two prevalent structure designs: a classical neural network model (MLP) and a Transformer encoder-based model (MLP+TL). For molecular encoding methods, we investigated several types of fingerprints, including traditional topological fingerprints, substructure-based fingerprints, and string-based representations. These two models were trained on seven well-known molecular datasets to evaluate different input molecular encoding methods ba...
| Authors | Sheng-Ya Chen, Shan-Ju Yeh |
|---|---|
| Primary category | q-bio.QM |
| Published | 8 June 2026 |
| Last updated at source | 8 June 2026 |
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