eChem: A Notebook Exploration of Quantum Chemistry

Thomas Fransson, Mickael G. Delcey, Iulia Emilia Brumboiu, Manuel Hodecker, Xin Li, Zilvinas Rinkevicius, Andreas Dreuw, Young Min Rhee, and Patrick Norman (2023)
Highlighted by Jan Jensen

eChem is an e-book that mixes text and code to teach quantum chemistry. The code is based on VeloxChem, which is a Python-based open source quantum chemistry software package. 

While you can use VeloxChem to perform standard quantum chemical calculations, the really cool thing is that it gives you easy access to the basis set, integrals and orbitals, DFT grids and functionals, etc. This in turn allows you to write your own SCF or Kohn-Sham-SCF procedure. It's sorta like Szabo and Ostlund updated and taken to the next level. 

If you truly want to understand quantum chemistry this is the way to go! One of the co-authors, Xin Li, very kindly got it working on Google Colab, so it is very easy to start playing around with it yourself. 

This work is licensed under a Creative Commons Attribution 4.0 International License.

Prediction of High-Yielding Single-Step or Cascade Pericyclic Reactions for the Synthesis of Complex Synthetic Targets

Tsuyoshi Mita, Hideaki Takano, Hiroki Hayashi, Wataru Kanna, Yu Harabuchi, K. N. Houk, and Satoshi Maeda (2022)
Highlighted by Jan Jensen

This paper has been on my to-do list for a while, but Derek Lowe beat me to it (again). DFT-based reaction prediction has yet to make an impact on synthesis planning due to the fact that many are complexities we still have to deal with efficiently, such as solvent effects in ionic mechanisms (very hard to predict accurately), catalysts and additives, chirality, and, well, just the sheer size of the reaction space. 

While these things will be dealt with in good time, it makes sense to see if there are any low-hanging fruits that can be picked under the current limitations, that still have "real life" applications. And this study did just that, by choosing pericyclic reactions. These are very popular reactions in organic synthesis, but require no catalysts nor additives and have minimal solvent effects. Furthermore, some use cases of this reaction in natural product synthesis can be very hard to spot, even for seasoned synthetic chemists, and the authors show that their algorithm can predict it a priori. So this could potentially be a useful tool for specific types synthesis planning.



This work is licensed under a Creative Commons Attribution 4.0 International License.

Machine-Learning-Guided Discovery of Electrochemical Reactions

Andrew F. Zahrt, Yiming Mo, Kakasaheb Y. Nandiwale, Ron Shprints, Esther Heid, and Klavs F. Jensen (2022)
Highlighted by Jan Jensen

Derek Lowe has highlighted the chemical aspects of this work already, so here I focus on the machine learning, which is pretty interesting. The authors want to predict whether a molecule will react with 4-dicyanobenzene anion after it is oxized at a cathode. They have 141 data points of which 42% show a reaction.

They tested several classification models using Morgan fingerprints as the molecular representation, but got at accuracy of only 60%. The then reasoned that the accuracy could be improved by using DFT features. However, rather than using molecular features they decided to use atomic features from an NBO analysis on the radical cation, neutral, radical anion. The feature vector was then tested on several data sets and shown to perform well.

The question is then how to combine the atomic feature vectors to a molecular representation for the reaction classification. The usual way is graph convolution but that'll require more than 141 data points to optimise. So instead they use graph2vec, which is an unsupervised learning method so it is easy to create arbitrarily large training sets. Graph2vec is analogous to word2vec (or, more accurately, doc2vec) which creates vector representations of words by predicting context in text (i.e. words that often appear close to the word of interest). For graph2vec the context is subgraphs of the input graph. 

The graph2vec embedder was then trained on 38k molecules (note that this requires 38k DFT calculations). Using this representation, the accuracy for the reaction classifier increased to 74%, which is a significant improvement compared to Morgan fingerprints. The classifier was then applied to the 38k molecules and 824 were predicted to be reactive. Twenty of these were selected for experimental validation and 16 (80%) were shown to be reactive. That's not a bad hit rate!

I was not aware of graph2vec before reading this paper and it seems like a very promising alternative to graph convolution, especially in the low data regime.

This work is licensed under a Creative Commons Attribution 4.0 International License.