Conformer-RL: A deep reinforcement learning library for conformer generation

Conformer‐RL is an open‐source Python package for applying deep reinforcement learning (RL) to the task of generating a diverse set of low‐energy conformations for a single molecule. The library features a simple interface to train a deep RL conformer generation model on any covalently bonded molecule or polymer, including most drug‐like molecules. Under the hood, it implements state‐of‐the‐art RL algorithms and graph neural network architectures tuned specifically for molecular structures. Conformer‐RL is also a platform for researching new algorithms and neural network architectures for conformer generation, as the library contains modular class interfaces for RL environments and agents, allowing users to easily swap components with their own implementations. Additionally, it comes with tools to visualize and save generated conformers for further analysis. Conformer‐RL is well‐tested and thoroughly documented with tutorials for each of the functionalities mentioned above, and is available on PyPi and Github: https://github.com/ZimmermanGroup/conformer-rl.

Learning To Predict Reaction Conditions: Relationships between Solvent, Molecular Structure, and Catalyst

Reaction databases provide a great deal of useful information to assist planning of experiments but do not provide any interpretation or chemical concepts to accompany this information. In this work, reactions are labeled with experimental conditions, and network analysis shows that consistencies within clusters of data points can be leveraged to organize this information. In particular, this analysis shows how particular experimental conditions (specifically solvent) are effective in enabling specific organic reactions (Friedel-Crafts, Aldol addition, Claisen condensation, Diels-Alder, and Wittig), including variations within each reaction class. Network analysis shows data points for reactions tend to break into clusters that depend on the catalyst and chemical structure. This type of clustering, which mimics how a chemist reasons, is derived directly from the network. Therefore, the findings of this work could augment synthesis planning by providing predictions in a fashion that mimics human chemists. To numerically evaluate solvent prediction ability, three methods are compared: network analysis (through the k-nearest neighbor algorithm), a support vector machine, and a deep neural network. The most accurate method in 4 of the 5 test cases is the network analysis, with deep neural networks also showing good prediction scores. The network analysis tool was evaluated by an expert panel of chemists, who generally agreed that the algorithm produced accurate solvent choices while simultaneously being transparent in the underlying reasons for its predictions.

CytoSEED: a Cytoscape plugin for viewing, manipulating and analyzing metabolic models created by the Model SEED

CytoSEED is a Cytoscape plugin for viewing, manipulating and analyzing metabolic models created using the Model SEED. The CytoSEED plugin enables users of the Model SEED to create informative visualizations of the reaction networks generated for their organisms of interest. These visualizations are useful for understanding organism-specific biochemistry and for highlighting the results of flux variability analysis experiments.