Amol Thakkar
Amol Thakkar
IBM Research
Verified email at
Cited by
Cited by
Molecular representations in AI-driven drug discovery: a review and practical guide
L David, A Thakkar, R Mercado, O Engkvist
Journal of Cheminformatics 12 (1), 56, 2020
AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning
S Genheden, A Thakkar, V Chadimová, JL Reymond, O Engkvist, ...
Journal of cheminformatics 12 (1), 70, 2020
Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain
A Thakkar, T Kogej, JL Reymond, O Engkvist, EJ Bjerrum
Chemical science 11 (1), 154-168, 2020
Retrosynthetic accessibility score (RAscore)–rapid machine learned synthesizability classification from AI driven retrosynthetic planning
A Thakkar, V Chadimová, EJ Bjerrum, O Engkvist, JL Reymond
Chemical science 12 (9), 3339-3349, 2021
Artificial intelligence and automation in computer aided synthesis planning
A Thakkar, S Johansson, K Jorner, D Buttar, JL Reymond, O Engkvist
Reaction chemistry & engineering 6 (1), 27-51, 2021
AI-assisted synthesis prediction
S Johansson, A Thakkar, T Kogej, E Bjerrum, S Genheden, T Bastys, ...
Drug Discovery Today: Technologies 32, 65-72, 2019
“Ring breaker”: neural network driven synthesis prediction of the ring system chemical space
A Thakkar, N Selmi, JL Reymond, O Engkvist, EJ Bjerrum
Journal of medicinal chemistry 63 (16), 8791-8808, 2020
Unbiasing retrosynthesis language models with disconnection prompts
A Thakkar, AC Vaucher, A Byekwaso, P Schwaller, A Toniato, T Laino
ACS Central Science 9 (7), 1488-1498, 2023
Levenshtein augmentation improves performance of smiles based deep-learning synthesis prediction
D Sumner, J He, A Thakkar, O Engkvist, EJ Bjerrum
Artificial Applicability Labels for Improving Policies in Retrosynthesis Prediction
EJ Bjerrum, A Thakkar, O Engkvist
Machine Learning: Science and Technology 2 (1), 2020
Rxnutils–a cheminformatics python library for manipulating chemical reaction data
C Kannas, S Genheden
How AI for synthesis can help tackle challenges in molecular discovery: Medicinal chemistry and chemical biology highlights
A Thakkar, P Schwaller
Chimia 75 (7-8), 677-677, 2021
Neural Network Guided Tree-Search Policies for Synthesis Planning
A Thakkar, EJ Bjerrum, O Engkvist, JL Reymond
International Conference on Artificial Neural Networks, 721-724, 2019
Modelling uranyl chemistry in liquid ammonia from density functional theory
N Sieffert, A Thakkar, M Bühl
Chemical communications 54 (74), 10431-10434, 2018
Standardizing chemical compounds with language models
MT Cretu, A Toniato, A Thakkar, AA Debabeche, T Laino, AC Vaucher
Machine Learning: Science and Technology 4 (3), 035014, 2023
Tools for Synthesis Planning, Automation, and Analytical Data Analysis
M Cretu, M Alberts, A Chakraborty, A Leonov, A Thakkar, T Laino
Chimia 77 (1/2), 17-21, 2023
Data-driven Reaction Template Fingerprints
A Chakraborty, A Thakkar, AC Vaucher, T Laino
Automatic Extraction of Reaction Templates for Synthesis Prediction
A Thakkar, JL Reymond
Chimia 76 (4), 294-294, 2022
Using Foundation Models to Promote Digitization and Reproducibility in Scientific Experimentation
A Thakkar, A Giovannini, A Foncubierta, C Baldassari, D Christofidellis, ...
NeurIPS 2023 AI for Science Workshop, 2023
Creating labs that learn through automated data management
A Thakkar, A Giovannini, M Manica, A Vaucher, P Ruch, T Laino
American Chemical Society (ACS) Fall Meeting, 2023
The system can't perform the operation now. Try again later.
Articles 1–20