Journal ArticleDOI
Scaffold‐directed face selectivity Machine‐Learned from vectors of non‐covalent interactions
TLDR
In this article, a method to vectorize and machine-learn non-covalent interactions responsible for scaffold-directed reactions important in synthetic chemistry is described, and models trained on this representation predict correct face of approach in ca. 90% of Michael additions or Diels-Alder cycloadditions.Abstract:
This work describes a method to vectorize and Machine-Learn, ML, non-covalent interactions responsible for scaffold-directed reactions important in synthetic chemistry. Models trained on this representation predict correct face of approach in ca. 90 % of Michael additions or Diels-Alder cycloadditions. These accuracies are significantly higher than those based on traditional ML descriptors, energetic calculations, or intuition of experienced synthetic chemists. Our results also emphasize the importance of ML models being provided with relevant mechanistic knowledge; without such knowledge, these models cannot easily "transfer-learn" and extrapolate to previously unseen reaction mechanisms.read more
Citations
More filters
Journal ArticleDOI
Machine Learning May Sometimes Simply Capture Literature Popularity Trends: A Case Study of Heterocyclic Suzuki–Miyaura Coupling
Wiktor Beker,Rafał Roszak,Agnieszka Wolos,Nicholas H. Angello,Vandana Rathore,Martin D. Burke,Bartosz A. Grzybowski +6 more
TL;DR: It is shown that ML models cannot offer any meaningful predictions of optimum reaction conditions, even if the search space is restricted to only solvents and bases, and highlighted the likely importance of systematically generating reliable and standardized data sets for algorithm training.
Journal ArticleDOI
Towards Data-Driven Design of Asymmetric Hydrogenation of Olefins: Database and Hierarchical Learning
TL;DR: In this article, the authors developed a hierarchical learning approach to achieve predictive machine leaning model using only dozens of enantioselectivity data with the target olefin, which offers a useful solution for the few-shot learning problem.
Journal ArticleDOI
Is Organic Chemistry Really Growing Exponentially
TL;DR: Trends in the function of time, reaction-type "popularity" and complexity based on the algorithm that extracts generalized reaction class templates are studied, useful in the context of computer-assisted synthesis, machine learning, and also for identifying erroneous entries in reaction databases.
Journal ArticleDOI
Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands.
Jordan J. Dotson,Lucy van Dijk,Jacob C. Timmerman,Samantha Grosslight,Richard C. Walroth,Francis Gosselin,Kurt Püntener,Kyle A. Mack,Matthew S. Sigman +8 more
TL;DR: In this article , a machine learning workflow for the multi-objective optimization of catalytic reactions that employ chiral bisphosphine ligands was described through the optimization of two sequential reactions required in the asymmetric synthesis of an active pharmaceutical ingredient.
Journal ArticleDOI
Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis
TL;DR: A recent review as mentioned in this paper summarizes the cutting-edge embedding techniques and model designs in synthetic performance prediction, elaborating how chemical knowledge can be incorporated into machine learning until June 2022.
References
More filters
Journal ArticleDOI
Machine Learning in Computer-Aided Synthesis Planning
TL;DR: Two critical aspects of CASP and recent machine learning approaches to both challenges are focused on, including the problem of retrosynthetic planning and anticipating the products of chemical reactions, which can be used to validate proposed reactions in a computer-generated synthesis plan.
Journal ArticleDOI
Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition.
TL;DR: Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment-independent and thus can also be easily used for proteins with low sequence similarities.
Journal ArticleDOI
Neural Networks for the Prediction of Organic Chemistry Reactions
TL;DR: This work explores the use of neural networks for predicting reaction types, using a new reaction fingerprinting method and combines this predictor with SMARTS transformations to build a system which, given a set of reagents and reactants, predicts the likely products.
Journal ArticleDOI
DScribe: Library of descriptors for machine learning in materials science
Lauri Himanen,Marc O. J. Jäger,Eiaki V. Morooka,Filippo Federici Canova,Yashasvi S. Ranawat,David Z. Gao,Patrick Rinke,Patrick Rinke,Adam S. Foster,Adam S. Foster +9 more
TL;DR: DScribe as discussed by the authors is a software package for machine learning that provides popular feature transformations (descriptors) for atomistic materials simulations, including Coulomb matrix, Ewald sum matrix, sine matrix, many-body tensor representation (MBTR), Atom-centered symmetry function (ACSF), and Smooth Overlap of Atomic Positions (SOAP).
Journal ArticleDOI
Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning
Andrew F. Zahrt,Jeremy J. Henle,Brennan T. Rose,Yang Wang,William T. Darrow,Scott E. Denmark +5 more
TL;DR: The ability to accurately predict a selective catalyst by using a set of less than optimal data remains a major goal for machine learning with respect to asymmetric catalysis and a framework for more efficient, predictive optimization is presented.