Marwin H. S. Segler

TL;DR: It is found that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art.

TL;DR: This work combines Monte Carlo tree search with an expansion policy network that guides the search, and a filter network to pre-select the most promising retrosynthetic steps that solve for almost twice as many molecules, thirty times faster than the traditional computer-aided search method.

TL;DR: This work shows that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing, and demonstrates that the properties of the generated molecules correlate very well with those of the molecules used to train the model.

TL;DR: It is reported that deep neural networks can learn to resolve reactivity conflicts and to prioritize the most suitable transformation rules.

TL;DR: GuacaMol as discussed by the authors is an evaluation framework for de novo molecular design based on a suite of standardized benchmarks, measuring the fidelity of the models to reproduce the property distribution of the training sets, the ability to generate novel molecules, the exploration and exploitation of chemical space, and a variety of single and multiobjective optimization tasks.