Corrigendum to “Modeling PROTAC degradation activity with machine learning” [Artif. Intell. Life Sci. 6 (2024) 100104]-Reference-Cited by-同舟云学术

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Corrigendum to “Modeling PROTAC degradation activity with machine learning” [Artif. Intell. Life Sci. 6 (2024) 100104]

Published:2024-08 Issue: Volume: Page:100114
ISSN:2667-3185
Container-title:Artificial Intelligence in the Life Sciences
language:en
Short-container-title:Artificial Intelligence in the Life Sciences

Author:

Ribes Stefano^ORCID,Nittinger Eva^ORCID,Tyrchan Christian^ORCID,Mercado Rocío^ORCID

Publisher

Elsevier BV

Reference30 articles.

1. Efficient multi-objective molecular optimization in a continuous latent space;Winter;Chem Sci,2019

2. De novo drug design using reinforcement learning with graph-based deep generative models;Atance;J Chem Inf Model,2022

3. Sample efficiency matters: A benchmark for practical molecular optimization;Gao;Adv Neural Inf Process Syst,2022

4. Computer-aided multi-objective optimization in small molecule discovery;Fromer;Patterns,2023

5. Benchmarking active learning protocols for ligand-binding affinity prediction;Gorantla;J Chem Inf Model,2024

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