A user-tunable machine learning framework for step-wise synthesis planning

Digital Discovery 2026 Open access

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Authors: Shiv Prakash, Nandan Patel, Hans‐Arno Jacobsen, Viki Kumar Prasad
DOI: 10.1039/d5dd00562k
Cited by: 0

Abstract

A mordern Hopfield network-based retrosynthetic tool for computer-aided synthesis planning that associates target molecules with reaction templates and uses tunable scoring of cost, temperature, and solvent toxicity to prioritize synthetic routes.

Fields of Study & Subjects

Machine Learning in Materials Science Computational Drug Discovery Methods Microbial Natural Products and Biosynthesis

Publication details

Journal / source
Digital Discovery
Volume
5
Issue
6
Pages
2669–2683
Publication date
1 January 2026
Language
EN