Rocío Mercado

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 11

Top Topics

Lessons Learned

Maximum Entropy Principle

Top Venues

J. Chem. Inf. Model.

Mach. Learn. Sci. Technol.

J. Cheminformatics

Publications

Annie M. Westerlund, Siva Manohar Koki, Supriya Kancharla, Alessandro Tibo, Lakshidaa Saigiridharan, Mikhail Kabeshov, Rocío Mercado, Samuel Genheden
Do Chemformers Dream of Organic Matter? Evaluating a Transformer Model for Multistep Retrosynthesis. J. Chem. Inf. Model. 64 (8) (2024)
Yossra Gharbi, Rocío Mercado
A Comprehensive Review of Emerging Approaches in Machine Learning for De Novo PROTAC Design. CoRR (2024)
Rocío Mercado, Steven M. Kearnes, Connor W. Coley
Data Sharing in Chemistry: Lessons Learned and a Case for Mandating Structured Reaction Data. J. Chem. Inf. Model. 63 (14) (2023)
Rocío Mercado, Esben Jannik Bjerrum, Ola Engkvist
Exploring Graph Traversal Algorithms in Graph-Based Molecular Generation. J. Chem. Inf. Model. 62 (9) (2022)
Wenhao Gao, Rocío Mercado, Connor W. Coley
Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design. ICLR (2022)
Sara Romeo Atance, Juan Viguera Diez, Ola Engkvist, Simon Olsson, Rocío Mercado
Drug Design Using Reinforcement Learning with Graph-Based Deep Generative Models. J. Chem. Inf. Model. 62 (20) (2022)
Divya Nori, Connor W. Coley, Rocío Mercado
De novo PROTAC design using graph-based deep generative models. CoRR (2022)
Rocío Mercado, Tobias Rastemo, Edvard Lindelöf, Günter Klambauer, Ola Engkvist, Hongming Chen, Esben Jannik Bjerrum
Graph networks for molecular design. Mach. Learn. Sci. Technol. 2 (2) (2021)
Wenhao Gao, Rocío Mercado, Connor W. Coley
Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design. CoRR (2021)
Jie Zhang, Rocío Mercado, Ola Engkvist, Hongming Chen
Comparative Study of Deep Generative Models on Chemical Space Coverage. J. Chem. Inf. Model. 61 (6) (2021)
Laurianne David, Amol Thakkar, Rocío Mercado, Ola Engkvist
Molecular representations in AI-driven drug discovery: a review and practical guide. J. Cheminformatics 12 (1) (2020)