| 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 | 465 | 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 | 272 | 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 | 140 | 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 | 134 | 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 | 62 | 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 | 51 | 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 | 31 | 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 | 21 | 2023 |
| Levenshtein augmentation improves performance of smiles based deep-learning synthesis prediction D Sumner, J He, A Thakkar, O Engkvist, EJ Bjerrum | 9 | 2020 |
| Artificial Applicability Labels for Improving Policies in Retrosynthesis Prediction EJ Bjerrum, A Thakkar, O Engkvist Machine Learning: Science and Technology 2 (1), 2020 | 8 | 2020 |
| Rxnutils–a cheminformatics python library for manipulating chemical reaction data C Kannas, S Genheden | 5 | 2022 |
| Automatic Extraction of Reaction Templates for Synthesis Prediction A Thakkar, JL Reymond Chimia 76 (4), 294-297, 2022 | 3 | 2022 |
| Modelling uranyl chemistry in liquid ammonia from density functional theory N Sieffert, A Thakkar, M Bühl Chemical Communications 54 (74), 10431-10434, 2018 | 3 | 2018 |
| 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 | 2 | 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 | 2 | 2019 |
| 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 | 1 | 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 | 1 | 2023 |
| Data-driven Reaction Template Fingerprints A Chakraborty, A Thakkar, AC Vaucher, T Laino | 1 | 2022 |
| Multimodal foundation models for more reproducible scientific experimentation and data capture P Ruch, G Gabrieli, IE Morales, D Christofidellis, M Graziani, A Giovannini, ... Future Labs Live, 2024 | | 2024 |
| Neural Template Extraction-Learning the Language of SMIRKS A Thakkar, T Laino | | 2024 |
Ola EngkvistAstraZeneca R&D Gothenburg Orcid:0000-0003-4970-6461Email verificata su astrazeneca.com