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Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, the last two decades have showed an increased interest from the community. This review gives an overview of the work in the field of ML in organocatalysis. The review starts by giving a short primer on ML for experimental chemists, before discussing its application for predicting the selectivity of organocatalytic transformations. Subsequently, we review ML employed for privileged catalysts, before focusing on its application for catalyst and reaction design. Concluding, we give our view on current challenges and future directions for this field, drawing inspiration from the application of ML to other scientific domains.

The concept of contrastive explanations originating from human reasoning is used in explainable artificial intelligence. In machine learning, contrastive explanations relate alternative prediction outcomes to each other involving the identification of features leading to opposing model decisions. We introduce a methodological framework for deriving contrastive explanations for machine learning models in chemistry to systematically generate intuitive explanations of predictions in high-dimensional feature spaces. The molecular contrastive explanations (MolCE) methodology explores alternative model decisions by generating virtual analogues of test compounds through replacements of molecular building blocks and quantifies the degree of “contrastive shifts” resulting from changes in model probability distributions. In a proof-of-concept study, MolCE was applied to explain selectivity predictions of ligands of D2-like dopamine receptor isoforms. Scientific contribution We introduce the first approach for generating contrastive explanations of machine learning models in chemistry. The methodology generates explanations of predictions that are readily accessible, chemically intuitive, and interpretable at the molecular level of detail.

The rational design of cyclin-dependent protein kinase (CDK) inhibitors presumes the development of approaches for accurate prediction of selectivity and the activity of small molecular weight anticancer drug candidates. Aiming at attenuation of general toxicity of low selectivity compounds, we herein explored the new chemotype of imidazole-4-N-acetamide substituted derivatives of the pan-CDK inhibitor PHA-793887. Newly synthesized compounds 1–4 containing an aliphatic methyl group or aromatic radicals at the periphery of the scaffold were analyzed for the prediction of relative free energies of binding to CDK1, -2, -5, and -9 using a protocol based on non-equilibrium (NEQ) thermodynamics. This methodology allows for the demonstration of a good correlation between the calculated parameters of interaction of 1–4 with individual targets and the values of inhibitory potencies in in vitro kinase assays. We provide evidence in support of NEQ thermodynamics as a time sparing, precise, and productive approach for generating chemical inhibitors of clinically relevant anticancer targets.

Though our current extinction crisis is substantial, it pales in comparison to the largest extinction in Earth's history, which occurred at the end of the Permian Period. Referred to as the “Great Dying,” this event saw the loss of up to 96% of all marine species and 70% of terrestrial species. Penn et al. explored the extinction dynamics of the time using Earth system models in conjunction with physiological data across animal taxa (see the Perspective by Kump). They conclude that increased marine temperatures and reduced oxygen availability were responsible for a majority of the recorded extinctions. Because similar environmental alterations are predicted outcomes of current climate change, we would be wise to take note. Science , this issue p. eaat1327 ; see also p. 1113 Increased temperature and reduced oxygen drove extinctions during the “Great Dying” about 252 million years ago. Rapid climate change at the end of the Permian Period (~252 million years ago) is the hypothesized trigger for the largest mass extinction in Earth’s history. We present model simulations of the Permian/Triassic climate transition that reproduce the ocean warming and oxygen (O 2 ) loss indicated by the geologic record. The effect of these changes on animal survival is evaluated using the Metabolic Index (Φ), a measure of scope for aerobic activity governed by organismal traits sampled in diverse modern species. Modeled loss of aerobic habitat predicts lower extinction intensity in the tropics, a pattern confirmed with a spatially explicit analysis of the marine fossil record. The combined physiological stresses of ocean warming and O 2 loss can account for more than half the magnitude of the “Great Dying.”

Many sensory-driven behaviors rely on predictions about future states of the environment. Visual input typically evolves along complex temporal trajectories that are difficult to extrapolate. We test the hypothesis that spatial processing mechanisms in the early visual system facilitate prediction by constructing neural representations that follow straighter temporal trajectories. We recorded V1 population activity in anesthetized macaques while presenting static frames taken from brief video clips, and developed a procedure to measure the curvature of the associated neural population trajectory. We found that V1 populations straighten naturally occurring image sequences, but entangle artificial sequences that contain unnatural temporal transformations. We show that these effects arise in part from computational mechanisms that underlie the stimulus selectivity of V1 cells. Together, our findings reveal that the early visual system uses a set of specialized computations to build representations that can support prediction in the natural environment. Many behaviours depend on predictions about the environment. Here the authors find neural populations in primary visual cortex to straighten the temporal trajectories of natural video clips, facilitating the extrapolation of past observations.

Compound–protein interactions (CPI) play significant roles in drug development. To avoid side effects, it is also crucial to evaluate drug selectivity when binding to different targets. However, most selectivity prediction models are constructed for specific targets with limited data. In this study, we present a pretrained multi-functional model for compound–protein interaction prediction (PMF-CPI) and fine-tune it to assess drug selectivity. This model uses recurrent neural networks to process the protein embedding based on the pretrained language model TAPE, extracts molecular information from a graph encoder, and produces the output from dense layers. PMF-CPI obtained the best performance compared to outstanding approaches on both the binding affinity regression and CPI classification tasks. Meanwhile, we apply the model to analyzing drug selectivity after fine-tuning it on three datasets related to specific targets, including human cytochrome P450s. The study shows that PMF-CPI can accurately predict different drug affinities or opposite interactions toward similar targets, recognizing selective drugs for precise therapeutics.Kindly confirm if corresponding authors affiliations are identified correctly and amend if any.Yes, it is correct.

Predictive processing posits that the brain minimizes discrepancies between internal predictions and sensory inputs, offering a unifying account of perception, cognition, and action. In voluntary actions, it is thought to suppress self-generated sensory outcomes. Although sensory mismatch signals have been extensively investigated and modeled, mechanistic insights into the neural computation of predictive processing in voluntary actions remain limited. We developed a computational model comprising two-compartment excitatory pyramidal cells (PCs) and three major types of inhibitory interneurons with biologically realistic connectivity. The model incorporates experience-dependent inhibitory plasticity and feature selectivity to shape excitation-inhibition (E/I) balance. We then extended it to a two-dimensional prediction-error (PE) circuit in which each PC has two segregated, top-down modulated dendrites-each bell-tuned to a distinct feature-enabling combination selectivity. The model reveals that top-down predictions can selectively suppress PCs with matching feature selectivity via experience-dependent inhibitory plasticity. This suppression depends on the response selectivity of inhibitory interneurons and on balanced excitation and inhibition across multiple pathways. The framework also accommodates predictions involving two independent features. By combining biological connectivity data with computational modeling, this study provides insights into the neural circuits and computations underlying the active suppression of sensory responses in voluntary actions. These findings contribute to understanding how the brain generates and processes predictions to guide behavior.

Accurately predicting the binding affinities of small organic molecules to biological macromolecules can greatly accelerate drug discovery by reducing the number of compounds that must be synthesized to realize desired potency and selectivity goals. Unfortunately, the process of assessing the accuracy of current computational approaches to affinity prediction against binding data to biological macromolecules is frustrated by several challenges, such as slow conformational dynamics, multiple titratable groups, and the lack of high-quality blinded datasets. Over the last several SAMPL blind challenge exercises, host–guest systems have emerged as a practical and effective way to circumvent these challenges in assessing the predictive performance of current-generation quantitative modeling tools, while still providing systems capable of possessing tight binding affinities. Here, we present an overview of the SAMPL6 host–guest binding affinity prediction challenge, which featured three supramolecular hosts: octa-acid (OA), the closely related tetra-endo-methyl-octa-acid (TEMOA), and cucurbit[8]uril (CB8), along with 21 small organic guest molecules. A total of 119 entries were received from ten participating groups employing a variety of methods that spanned from electronic structure and movable type calculations in implicit solvent to alchemical and potential of mean force strategies using empirical force fields with explicit solvent models. While empirical models tended to obtain better performance than first-principle methods, it was not possible to identify a single approach that consistently provided superior results across all host–guest systems and statistical metrics. Moreover, the accuracy of the methodologies generally displayed a substantial dependence on the system considered, emphasizing the need for host diversity in blind evaluations. Several entries exploited previous experimental measurements of similar host–guest systems in an effort to improve their physical-based predictions via some manner of rudimentary machine learning; while this strategy succeeded in reducing systematic errors, it did not correspond to an improvement in statistical correlation. Comparison to previous rounds of the host–guest binding free energy challenge highlights an overall improvement in the correlation obtained by the affinity predictions for OA and TEMOA systems, but a surprising lack of improvement regarding root mean square error over the past several challenge rounds. The data suggests that further refinement of force field parameters, as well as improved treatment of chemical effects (e.g., buffer salt conditions, protonation states), may be required to further enhance predictive accuracy.

A simple and fast method is described for the extraction of polycyclic aromatic hydrocarbons (PAHs) from complex samples. It is based on the use of a nanoferrofluid modified with a ternary hydrophobic deep eutectic solvent. A predictive model was used for the selection of the optimal eutectic mixture. The entire microextraction only takes a few minutes for completion. Under the optimal extraction conditions (by using menthol, borneol and camphor in a molar ratio of 5:1:4; 80 mg of nanoferrofluid), it offers marked improvements in terms of selectivity and sensitivity. The limits of detection range between 0.31 and 5.9 ng·L −1 , and recoveries from spiked samples between 91.3 and 121%. In addition, the strong interactions between PAHs and the extractant were supported by quantum mechanical calculations. This results in a better insight into the microextraction mechanism, providing a fast, environmentally friendly and effective route for the optimization of pretreatment parameters. The method was successfully applied to the determination of the PAHs naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, chrysene, benzo[b]fluoranthene, benzo[a]pyrene, dibenzo[a,h]anthracene, benzo[g,h,i]perylene and indeno[1,2,3-c,d]pyrene in 12 kinds of coffee samples after different roasting conditions. Graphical abstract Schematic presentation of nanoferrofluid modified with ternary hydrophobic deep eutectic solvent and their application for selective microextraction of ultra-trace polycyclic aromatic hydrocarbons in coffee prior to HPLC analysis.