Using natural language processing (NLP)-inspired molecular embedding approach to predict Hansen solubility parameters-Reference-Cited by-同舟云学术

Using natural language processing (NLP)-inspired molecular embedding approach to predict Hansen solubility parameters

Published:2024 Issue:1 Volume:3 Page:145-154
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Pang Jiayun¹^ORCID,Pine Alexander W. R.¹,Sulemana Abdulai¹

Affiliation:

1. School of Science, Faculty of Engineering and Science, University of Greenwich, Medway Campus, Central Avenue, Chatham Maritime, ME4 3RL, UK

Abstract

Hansen solubility parameters can be predicted with good accuracy using only the SMILES of molecules and a BERT deep learning model with finetuning.

Funder

Engineering and Physical Sciences Research Council

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D3DD00119A

Reference41 articles.

1. Language models can learn complex molecular distributions

2. Advances in machine learning for directed evolution

3. J.Vig , A.Madani , L. R.Varshney , C.Xiong , R.Socher and N. F.Rajani , BERTology Meets Biology: Interpreting Attention in Protein Language Models , 2020

4. I.Lee and H.Nam , Infusing Linguistic Knowledge of SMILES into Chemical Language Models , 2022

5. Learned protein embeddings for machine learning

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Designing Heterodiatomic Carbon Hydrangea Superstructures via Machine Learning‐Regulated Solvent‐Precursor Interactions for Superior Zinc Storage;Small;2024-08-23

2. Enhancing Hansen Solubility Predictions with Molecular and Graph-Based Approaches;Chemometrics and Intelligent Laboratory Systems;2024-08

3. Virtual Screening of Molecules via Neural Fingerprint-based Deep Learning Technique;2024-05-09