Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors

Abstract

Machine learning can predict the major regio-, site-, and diastereoselective outcomes of Diels-Alder reactions better than standardq uantum-mechanical methods and with accuracies exceeding 90 %provided that i) the diene/dienophile substrates are represented by "physical-organic" descriptors reflecting the electronic and steric characteristics of their substituents and ii)t he positions of such substituents relative to the reaction core are encoded ("vectorized") in an informative way.

Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies

“…We opted for physical organic chemistry features which would be chemically understandable and transferable to other reactions. 29 We selected features associated with nucleophilicity, electrophilicity, sterics, dispersion and bonding as well as features describing the solvent.…”

Computer-generated “synthetic contingency” plans at times of logistics and supply problems: scenarios for hydroxychloroquine and remdesivir

“…, both known and new) targets. The software combines elements of network theory 17 , 18 with an expert knowledge-base of synthetic transformations as well as multiple reaction-evaluation routines (based on machine learning, 12 , 13 quantum mechanics, 9 , 10 and molecular dynamics 10 , 14 ) to search over vast trees of synthetic possibilities. The reaction transforms (currently, ∼100 000) are expert-coded based on the underlying reaction mechanisms and are broader than any specific literature precedents (for comparison with machine extraction of rules from reaction repositories, see ref.…”

“…In addition, the failure of the worldwide logistics and supply chains that has accompanied the COVID-19 pandemic might render some key substrates temporarily unavailable, in effect delaying the execution of the proven synthetic routes and calling for alternative synthetic solutions. Anticipating such complications, we harnessed the power of Chematica [9][10][11][12][13][14][15][16][17][18] an experimentally tested 10,11 platform for computer-assisted retrosynthesis of both known and unknown target molecules to design syntheses of HCQ and remdesivir. We were most interested in synthetic plans that would (1) commence from various inexpensive and popular starting materials (so that the syntheses minimize the abovementioned supply problems); (2) circumvent patented methodologies whenever possible; 16 and (3) minimize the use of expensive methodologies and/or reagents.…”

Molekulares maschinelles Lernen: Die Zukunft der Synthesechemie?

“…Diese multimolekularen Probleme wie die Computer‐Optimierung von Reaktionsbedingungen sowie Vorhersagen über Ausgang (Abbildung c), Selektivität und Ausbeute chemischer Reaktionen sind Themen von allgemeinem Interesse, und obwohl erste Pionierarbeiten Chancen für potentielle ML‐Anwendungen aufzeigen, sind die entwickelten Hilfsmittel für den Laboralltag noch nicht ausreichend. Dies liegt an der Problematik, dass es Anwendungen typischerweise an drei Hauptpunkten mangelt:…”