PREFER: A New Predictive Modeling Framework for Molecular Discovery.
J Chem Inf Model
; 63(15): 4497-4504, 2023 08 14.
Article
in En
| MEDLINE
| ID: mdl-37487018
ABSTRACT
Machine-learning and deep-learning models have been extensively used in cheminformatics to predict molecular properties, to reduce the need for direct measurements, and to accelerate compound prioritization. However, different setups and frameworks and the large number of molecular representations make it difficult to properly evaluate, reproduce, and compare them. Here we present a new PREdictive modeling FramEwoRk for molecular discovery (PREFER), written in Python (version 3.7.7) and based on AutoSklearn (version 0.14.7), that allows comparison between different molecular representations and common machine-learning models. We provide an overview of the design of our framework and show exemplary use cases and results of several representation-model combinations on diverse data sets, both public and in-house. Finally, we discuss the use of PREFER on small data sets. The code of the framework is freely available on GitHub.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Machine Learning
/
Cheminformatics
Type of study:
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
J Chem Inf Model
Journal subject:
INFORMATICA MEDICA
/
QUIMICA
Year:
2023
Type:
Article
Affiliation country:
Switzerland