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Learning SMILES Semantics: Word2Vec and Transformer Embeddings for Molecular Property Prediction

This paper investigates the effectiveness of Word2Vec-based molecular representation learning on SMILES (Simplified Molecular Input Line Entry System) strings for a downstream prediction task related to the market approvability of chemical compounds. Here, market approvability is treated as a proxy...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Saya Hashemian, Zak Khan, Pulkit Kalhan, Yang Liu
Format: Artigo
Sprache:Inglês
Veröffentlicht: MDPI AG 2025-09-01
Schriftenreihe:Algorithms
Schlagworte:
SMILES
cheminformatics
Word2Vec
NLP
CBOW
molecular embedding
Online-Zugang:https://www.mdpi.com/1999-4893/18/9/547
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