Joshua Meyers and Nathan Brown (2024)Highlighted by Jan Jensen Figure 1 from the paper. (c) The authors. Reproduced under the CC-BY license Genetic algorithms (GAs) allow for changes at the atom level (as opposed to molecular fragments) allow for a very fine-grained search of chemical space. However, some of the resulting molecules are not chemically sensible and one usually has to include a synthetic accessibility constraint in the scoring function.  However, another approach is to use fragments and include synthetic accessibility in the fragmentation scheme, which is what this study d ..read more

Michael A. Skinnider (2024) Highlighted by Jan Jensen Figure 3c from the paper. (c) The author. Reproduced under the CC-BY License Language models (LMs) don't always produce valid SMILES and while for modern methods the percentage of invalid SMILES tends to be relatively small, much effort has been expended on making it as small as possible. SELFIES was invented as a way to make this percentage 0, since SELFIES is design to always produce valid SMILES. However, several studies have shown that SMILES-based LMs tends to produce molecular distributions that is closer to the training set, compar ..read more

Philipp M. Pflüger, Marius Kühnemund, Felix Katzenburg, Herbert Kuchen, and Frank Glorius (2024)Highlighted by Jan Jensen Parts of Figures 2 and 6 combined. (c) 2024 Elsevier, Inc This paper presents a very novel approach to XAI that allows for direct comparison with chemical intuition. Molecular fingerprints (either binary or count) are defined using randomly generated SMARTS patterns and then uses a genetic algorithm to find the optimum fingerprint of a certain length. Here the optimum is defined as the one giving the lowest error when used with CatBoost. The GA search requires many thous ..read more

Jason D. Shields, Rachel Howells, Gillian Lamont, Yin Leilei, Andrew Madin, Christopher E. Reimann, Hadi Rezaei, Tristan Reuillon, Bryony Smith, Clare Thomson, Yuting Zhengc and Robert E. Ziegler (2024) Highlighted by Jan Jensen Figure 3 from this paper (c) the authors 2020. Reproduced under the CC-BY license This is one of the rare papers where experimental chemists talk candidly about their experiences using ML models developed by others. In this case it is AiZynthFinder, which is developed at AstraZeneca Gothenburg and predicts retrosynthetic paths, while the users are most synt ..read more

Thijs Stuyver (2024) Highlighted by Jan Jensen Figure 2 from the paper. (c) the author 2024 reproduced under the CC-BY-NC-ND licence This paper caught my eye for several reasons. It's an open source implementation of Maeda's AFIR method, but modified for double-ended TS searches. The setup is completely automated and interfaced to  xTB so it is fast. It's applied to really challenging problems such as solvent assisted bimolecular reactions and uncovers some important shortcomings of the xTB method.  This work is licensed under a Creative Commons Attribution 4.0 International Lic ..read more

Chenru Duan, Yuanqi Du, Haojun Jia, and Heather J. Kulik (2023) Highlighted by Jan Jensen Part of Figure 1 from the paper.  As anyone who has tried it will know, finding TSs is one of the most difficult, fiddly, and frustrating tasks in computational chemistry. While there are several methods aimed at automating the process, they tend to have a mixed success rate or be computationally expensive and, often, both. This paper looks to be an important first step in the right direction. The method produces a guess at a TS structure based on the coordinates of the reactants and products. Nota ..read more

Federico Zipoli, Carlo Baldassari, Matteo Manica, Jannis Born, and Teodoro Laino (2023) Highlighted by Jan Jensen Part of Figure 10 from the paper. (c) The authors 2023. Reproduced under the CC-NC-ND Prediction of retrosynthetic reaction trees are typically done by stringing together individual retrosynthetic steps that have the highest predicted confidences. The confidence is typically related to the frequency of the reaction in the training set. This approach has two main problems that this paper addresses. One problem is that "rare" reactions are seldom selected even if they might actuall ..read more

Daniel Vella and Jean-Paul Ebejer (2023) Highlighted by Jan Jensen TOC graphic from the article This paper is an update and expansion to this seminal paper by Pande and co-workers (you should definitely read both). It compares the ability to distinguish active and inactive compounds for few-shots methods to more conventional approaches for very small datasets. It concludes that the former outperform the latter for some data sets and not for others, which is surprising given that few-shot methods are designed with very small data sets in mind. Few shot methods learn a graph-based embedding tha ..read more

Ryo Tamura, Kei Terayama, Masato Sumita, and Koji Tsuda (2023) Highlighted by Jan Jensen Figure 1 from the paper. (c) APS 2023. Reproduced under the CC-BY license. One of the main challenges in multi-objective optimisation is how to weigh the different objectives to get the desired results. Pareto optimisation can in principle solve this problem, but of you get too many solutions you have to select a subset for testing, which basically involves (manually) weighing the importance of each objective. This paper proposes a new way to select the potentially most interesting candidates. The idea i ..read more

Julian A. Hueffel, Theresa Sperger, Ignacio Funes-Ardoiz, Jas S. Ward, Kari Rissanen, Franziska Schoenebeck (2021) Highlighted by Jan Jensen Figure 1 from the paper. (c) 2021 the authors. I've been meaning to highlight this paper for years but forgot. However, in the last week k-means clustering came up twice in two completely unrelated contexts, which reminded me of this beautiful paper where the authors managed to use ML to make successful predictions based only five data points!  Pd catalysts can exist in either in a dimer or monomer form depending on the ligands and there are no he ..read more