đź“Ł Help Shape the Future of UKRI's Gateway to Research (GtR)

We're improving UKRI's Gateway to Research and are seeking your input! If you would be interested in being interviewed about the improvements we're making and to have your say about how we can make GtR more user-friendly, impactful, and effective for the Research and Innovation community, please email gateway@ukri.org.

Probabilistic graph-to-graph model for reaction prediction and retrosynthetic route prediction

Lead Research Organisation: University of Cambridge
Department Name: Engineering

Abstract

This project aims to develop machine learning methodologies that predict the outcomes of chemical reaction given reagent and reactant. We also aim solve the inverse problem of devising a sequence of reactions to make a target compound from simple starting materials.

People

ORCID iD
David Kovacs (Student)

Publications

10 25 50
publication icon
Kovács DP (2021) Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE. in Journal of chemical theory and computation

publication icon
Kovács DP (2021) Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias. in Nature communications

publication icon
Kovács D (2024) Machine Learning Force Fields for Molecular Chemistry

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/T517677/1 30/09/2019 29/09/2025
2276922 Studentship EP/T517677/1 30/09/2019 07/12/2023 David Kovacs