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NeurIPS'2021 Machine Learning for Structural Biology Workshop
2021.
Chapters
Зенкова Н. В., Sedykh E., Shugaeva T. et al., , in: NeurIPS'2021 Machine Learning for Structural Biology Workshop.: [б.и.], 2021.
Predicting a structure of an antibody from its sequence is important since it allows for a better design process of synthetic antibodies that play a vital role in the health industry. Most of the structure of an antibody is conservative. The most variable and hard-to-predict part is the {\it third complementarity-determining region of the antibody ...
Added: March 24, 2022
Language:
English
Keywords: machine learning for molecules
Tiapkin D., Morozov N., Naumov A. et al., , in: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (AISTATS 2024), 2-4 May 2024, Palau de Congressos, Valencia, Spain. PMLR: Volume 238Vol. 238.: Valencia: PMLR, 2024. P. 4213–4221.
The recently proposed generative flow networks (GFlowNets) are a method of training a policy to sample compositional discrete objects with probabilities proportional to a given reward via a sequence of actions. GFlowNets exploit the sequential nature of the problem, drawing parallels with reinforcement learning (RL). Our work extends the connection between RL and GFlowNets to ...
Added: June 22, 2024
van Tilborg D., Grisoni F., Journal of Chemical Information and Modeling 2022 Vol. 62 No. 23 P. 5938–5951
Machine learning has become a crucial tool in drug discovery and chemistry at large, e.g., to predict molecular properties, such as bioactivity, with high accuracy. However, activity cliffs─pairs of molecules that are highly similar in their structure but exhibit large differences in potency─have received limited attention for their effect on model performance. Not only are these ...
Added: December 22, 2022
[б.и.], 2020.
Discovering new molecules and materials is a central pillar of human well-being, providing new medicines, securing the world’s food supply via agrochemicals, or delivering new battery or solar panel materials to mitigate climate change. However, the discovery of new molecules for an application can often take up to a decade, with costs spiraling. Machine learning ...
Added: December 28, 2020