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Training algorithms to computationally plan multistep organic syntheses has been a challenge for more than 50 years 1 – 7 . However, the field has progressed greatly since the development of early programs such as LHASA 1 , 7 , for which reaction choices at each step were made by human operators. Multiple software platforms 6 , 8 – 14 are now capable of completely autonomous planning. But these programs ‘think’ only one step at a time and have so far been limited to relatively simple targets, the syntheses of which could arguably be designed by human chemists within minutes, without the help of a computer. Furthermore, no algorithm has yet been able to design plausible routes to complex natural products, for which much more far-sighted, multistep planning is necessary 15 , 16 and closely related literature precedents cannot be relied on. Here we demonstrate that such computational synthesis planning is possible, provided that the program’s knowledge of organic chemistry and data-based artificial intelligence routines are augmented with causal relationships 17 , 18 , allowing it to ‘strategize’ over multiple synthetic steps. Using a Turing-like test administered to synthesis experts, we show that the routes designed by such a program are largely indistinguishable from those designed by humans. We also successfully validated three computer-designed syntheses of natural products in the laboratory. Taken together, these results indicate that expert-level automated synthetic planning is feasible, pending continued improvements to the reaction knowledge base and further code optimization. A synthetic route-planning algorithm, augmented with causal relationships that allow it to strategize over multiple steps, can design complex natural-product syntheses that are indistinguishable from those designed by human experts.

Triaging unpromising lead molecules early in the drug discovery process is essential for accelerating its pace while avoiding the costs of unwarranted biological and clinical testing. Accordingly, medicinal chemists have been trying for decades to develop metrics—ranging from heuristic measures to machine-learning models—that could rapidly distinguish potential drugs from small molecules that lack drug-like features. However, none of these metrics has gained universal acceptance and the very idea of ‘drug-likeness’ has recently been put into question. Here, we evaluate drug-likeness using different sets of descriptors and different state-of-the-art classifiers, reaching an out-of-sample accuracy of 87–88%. Remarkably, because these individual classifiers yield different Bayesian error distributions, their combination and selection of minimal-variance predictions can increase the accuracy of distinguishing drug-like from non-drug-like molecules to 93%. Because total variance is comparable with its aleatoric contribution reflecting irreducible error inherent to the dataset (as opposed to the epistemic contribution due to the model itself), this level of accuracy is probably the upper limit achievable with the currently known collection of drugs. When designing new drugs, there are countless ways to create molecules, yet only a few interact with biological targets. Beker and colleagues provide here a graph neural network based metric for drug-likeness that can guide the search.

Carbocationic rearrangements are fundamental to both biosynthetic and synthetic chemistries, yet their high reactivity and mechanistic complexity often defy intuitive prediction of outcomes. Herein, HopCat, a hybrid algorithmic platform combining rule‐based transformation logic with quantum‐mechanically informed kinetics, is applied to explore the rearrangement networks of acid‐catalyzed terpenoid transformations. Using a series of terpenoid substrates, an array of structurally diverse and unprecedented products is obtained. It is narated  how HopCat can work synergistically with a human chemist to guide product assignment, downselect product candidates based on NMR cues, and reconstruct plausible, multistep mechanistic pathways, including those involving unanticipated intermediates.

Multi-metal oxides in general and perovskite oxides in particular have attracted considerable attention as oxygen evolution electrocatalysts. Although numerous theoretical studies have been undertaken, the most promising perovskite-based catalysts continue to emerge from human-driven experimental campaigns rather than data-driven machine learning protocols, which are often limited by the scarcity of experimental data on which to train the models. This work promises to break this impasse by demonstrating that active learning on even small datasets—but supplemented by informative structural-characterization data and coupled with closed-loop experimentation—can yield materials of outstanding performance. The model we develop not only reproduces several non-obvious and actively studied experimental trends but also identifies a composition of a perovskite oxide electrocatalyst exhibiting an intrinsic overpotential at 10 mA cm –2 oxide of 391 mV, which is among the lowest known of four-metal perovskite oxides. Multi-metal and perovskite oxides are attractive as oxygen evolution electrocatalysts, and thus far the most promising candidates have emerged from experimental methodologies. Active-learning models supplemented by structural-characterization data and closed-loop experimentation can now identify a perovskite oxide with outstanding performance.

Recent years have seen revived interest in computer-assisted organic synthesis 1 , 2 . The use of reaction- and neural-network algorithms that can plan multistep synthetic pathways have revolutionized this field 1 , 3 – 7 , including examples leading to advanced natural products 6 , 7 . Such methods typically operate on full, literature-derived ‘substrate(s)-to-product’ reaction rules and cannot be easily extended to the analysis of reaction mechanisms. Here we show that computers equipped with a comprehensive knowledge-base of mechanistic steps augmented by physical-organic chemistry rules, as well as quantum mechanical and kinetic calculations, can use a reaction-network approach to analyse the mechanisms of some of the most complex organic transformations: namely, cationic rearrangements. Such rearrangements are a cornerstone of organic chemistry textbooks and entail notable changes in the molecule’s carbon skeleton 8 – 12 . The algorithm we describe and deploy at https://HopCat.allchemy.net/ generates, within minutes, networks of possible mechanistic steps, traces plausible step sequences and calculates expected product distributions. We validate this algorithm by three sets of experiments whose analysis would probably prove challenging even to highly trained chemists: (1) predicting the outcomes of tail-to-head terpene (THT) cyclizations in which substantially different outcomes are encoded in modular precursors differing in minute structural details; (2) comparing the outcome of THT cyclizations in solution or in a supramolecular capsule; and (3) analysing complex reaction mixtures. Our results support a vision in which computers no longer just manipulate known reaction types 1 – 7 but will help rationalize and discover new, mechanistically complex transformations. Computers equipped with a comprehensive knowledge-base of mechanistic steps augmented by physical-organic chemistry rules, as well as quantum mechanical and kinetic calculations, can use a reaction-network approach to analyse the mechanisms of cationic rearrangements.

Catalytic fields illustrate topology of the optimal charge distribution of a molecular environment reducing the activation energy for any process involving barrier crossing, like chemical reaction, bond rotation etc. Until now, this technique has been successfully applied to predict catalytic effects resulting from intermolecular interactions with individual water molecules constituting the first hydration shell, aminoacid mutations in enzymes or Si→Al substitutions in zeolites. In this contribution, hydrogen to fluorine (H→F) substitution effects for two model reactions have been examined indicating qualitative applicability of the catalytic field concept in the case of systems involving intramolecular interactions. Graphical abstract Hydrogen to fluorine (H→F) substitution effects on activation energy in [kcal/mol]