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Malaria which is mainly caused by Plasmodium falciparum parasite remains a devastating public health concern, necessitating the need to develop new antimalarial agents. P. falciparum heat shock protein 90 (Hsp90), is indispensable for parasite survival and a promising drug target. Inhibitors targeting the ATP-binding pocket of the N-terminal domain have anti-Plasmodium effects. We proposed a de novo active learning (AL) driven method in tandem with docking to predict inhibitors with unique scaffolds and preferential selectivity towards PfHsp90. Reference compounds, predicted to bind PfHsp90 at the ATP-binding pocket and possessing anti-Plasmodium activities, were used to generate 10,000 unique derivatives and to build the Auto-quantitative structures activity relationships (QSAR) models. Glide docking was performed to predict the docking scores of the derivatives and > 15,000 compounds obtained from the ChEMBL database. Re-iterative training and testing of the models was performed until the optimum Kennel-based Partial Least Square (KPLS) regression model with a regression coefficient R2 = 0.75 for the training set and squared correlation prediction Q2 = 0.62 for the test set reached convergence. Rescoring using induced fit docking and molecular dynamics simulations enabled us to prioritize 15 ATP/ADP-like design ideas for purchase. The compounds exerted moderate activity towards P. falciparum NF54 strain with IC50 values of ≤ 6μM and displayed moderate to weak affinity towards PfHsp90 (KD range: 13.5–19.9μM) comparable to the reported affinity of ADP. The most potent compound was FTN-T5 (PfN54 IC50:1.44μM; HepG2/CHO cells SI≥ 29) which bound to PfHsp90 with moderate affinity (KD:7.7μM), providing a starting point for optimization efforts. Our work demonstrates the great utility of AL for the rapid identification of novel molecules for drug discovery (i.e., hit identification). The potency of FTN-T5 will be critical for designing species-selective inhibitors towards developing more efficient agents against malaria. [ABSTRACT FROM AUTHOR]

Nanoscopic systems exhibit diverse molecular substructures by which they facilitate specific functions. Theoretical models of them, which aim at describing, understanding, and predicting these capabilities, are difficult to build. Viable quantum-classical hybrid models come with specific challenges regarding atomistic structure construction and quantum region selection. Moreover, if their dynamics are mapped onto a state-to-state mechanism such as a chemical reaction network, its exhaustive exploration will be impossible due to the combinatorial explosion of the reaction space. Here, we introduce a "quantum magnifying glass" that allows one to interactively manipulate nanoscale structures at the quantum level. The quantum magnifying glass seamlessly combines autonomous model parametrization, ultra-fast quantum mechanical calculations, and automated reaction exploration. It represents an approach to investigate complex reaction sequences in a physically consistent manner with unprecedented effortlessness in real time. We demonstrate these features for reactions in bio-macromolecules and metal-organic frameworks, diverse systems that highlight general applicability. It is difficult to gain an atomistic understanding of reactions within nanoscopic systems due to their large size. Here, the authors introduce a computational framework, the quantum magnifying glass, that enables such studies to be carried out interactively in real time. [ABSTRACT FROM AUTHOR]

Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This renders brute-force explorations unfeasible, while explorations with completely pre-defined intermediates or hard-wired chemical constraints, such as element-specific coordination numbers, are not flexible enough for complex chemical systems. Here, we introduce a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to focus on specific regions of an emerging network. It also allows for guiding automated data generation in the context of mechanism exploration, catalyst design, and other chemical optimization challenges. The algorithm is demonstrated for reaction mechanism elucidation of transition metal catalysts. We highlight how to explore catalytic cycles in a systematic and reproducible way. The exploration objectives are fully adjustable, allowing one to harness the STEERING WHEEL for both structure-specific (accurate) calculations as well as for broad high-throughput screening of possible reaction intermediates. Automated reaction exploration is the key to systematic elucidation of chemical mechanisms. Here, the authors introduce a generally applicable algorithm to steer an automated exploration towards region of interest in chemical reaction space. [ABSTRACT FROM AUTHOR]

Polystyrene supported palladium catalysts were synthesized and their chemical and morphological nature were studied using NMR, XRD, TEM, EDX, and XPS analyses. Using the supported catalyst, the first palladium catalyzed acceptorless dehydrogenative coupling of secondary alcohols in water is reported. This method is green, sustainable, phosphine free, and carried out under aerobic condition. Reusability of the catalyst was shown for both alkylation and quinoline reactions till 7 cycles with marginal decrease in yield. Metal leaching was found to be the cause of decrease in yield in both instances. Polystyrene anchored palladium catalysts have been synthesized and used in the acceptorless dehydrogenative coupling of secondary alcohols in aqueous condition. Stability and recyclability of the catalyst was also studied up to 7th cycle in water. [ABSTRACT FROM AUTHOR]

Quantum mechanics/molecular mechanics (QM/MM) hybrid models allow one to address chemical phenomena in complex molecular environments. Whereas this modeling approach can cope with a large system size at moderate computational costs, the models are often tedious to construct and require manual preprocessing and expertise. As a result, transferability to new application areas can be limited and the many parameters are not easy to adjust to reference data that are typically scarce. Therefore, it is desirable to devise automated procedures of controllable accuracy, which enables such modeling in a standardized and black‐box‐type manner. Although diverse best‐practice protocols have been set up for the construction of individual components of a QM/MM model (e.g., the MM potential, the type of embedding, the choice of the QM region), automated procedures that reconcile all steps of the QM/MM model construction are still rare. Here, we review the state of the art of QM/MM modeling with a focus on automation. We elaborate on MM model parametrization, on atom‐economical physically‐motivated QM region selection, and on embedding schemes that incorporate mutual polarization as critical components of the QM/MM model. In view of the broad scope of the field, we mostly restrict the discussion to methodologies that build de novo models based on first‐principles data, on uncertainty quantification, and on error mitigation with a high potential for automation. Ultimately, it is desirable to be able to set up reliable QM/MM models in a fast and efficient automated way without being constrained by specific chemical or technical limitations. This article is categorized under:Electronic Structure Theory > Combined QM/MM Methods [ABSTRACT FROM AUTHOR]

SERENITY [J Comput Chem. 2018;39:788–798] is an open‐source quantum chemistry software that provides an extensive development platform focused on quantum‐mechanical multilevel and embedding approaches. In this study, we give an overview over the developments done in Serenity since its original publication in 2018. This includes efficient electronic‐structure methods for ground states such as multilevel domain‐based local pair natural orbital coupled cluster and Møller–Plesset perturbation theory as well as the multistate frozen‐density embedding quasi‐diabatization method. For the description of excited states, SERENITY features various subsystem‐based methods such as embedding variants of coupled time‐dependent density‐functional theory, approximate second‐order coupled cluster theory and the second‐order algebraic diagrammatic construction technique as well as GW/Bethe–Salpeter equation approaches. SERENITY's modular structure allows combining these methods with density‐functional theory (DFT)‐based embedding through various practical realizations and variants of subsystem DFT including frozen‐density embedding, potential‐reconstruction techniques and projection‐based embedding. This article is categorized under:Electronic Structure Theory > Density Functional TheoryElectronic Structure Theory > Ab Initio Electronic Structure MethodsSoftware > Quantum Chemistry [ABSTRACT FROM AUTHOR]

Although computational contributions to the understanding of organometallic homogeneous catalysts have become fairly routine, a step‐change in the application of computational methods would be to achieve efficient, robust, and reliable prediction of the outcome of catalytic transformations. While we concur that there have been a number of recent promising advances in the interactions between computational and experimental mechanistic studies, the mapping of reactivity space remains incomplete and large‐scale studies have to make limiting assumptions which restrict their transferability. Close synergies between characterization and analysis techniques which are integrated with computational data, along with data capture, curation, and exploitation, are vital and develop our understanding of all aspects of the catalytic pathways (including activation and deactivation) and allow the continual refinement of mechanistic understanding, challenged by testing predictions experimentally. Here we review recent examples to formulate a protocol for such interactions. This article is categorized under:Electronic Structure Theory > Ab Initio Electronic Structure MethodsStructure and Mechanism > Reaction Mechanisms and CatalysisData Science > Artificial Intelligence/Machine LearningElectronic Structure Theory > Density Functional Theory [ABSTRACT FROM AUTHOR]

Autonomous computations that rely on automated reaction network elucidation algorithms may pave the way to make computational catalysis on a par with experimental research in the field. Several advantages of this approach are key to catalysis: (i) automation allows one to consider orders of magnitude more structures in a systematic and open-ended fashion than what would be accessible by manual inspection. Eventually, full resolution in terms of structural varieties and conformations as well as with respect to the type and number of potentially important elementary reaction steps (including decomposition reactions that determine turnover numbers) may be achieved. (ii) Fast electronic structure methods with uncertainty quantification warrant high efficiency and reliability in order to not only deliver results quickly, but also to allow for predictive work. (iii) A high degree of autonomy reduces the amount of manual human work, processing errors, and human bias. Although being inherently unbiased, it is still steerable with respect to specific regions of an emerging network and with respect to the addition of new reactant species. This allows for a high fidelity of the formalization of some catalytic process and for surprising in silico discoveries. In this work, we first review the state of the art in computational catalysis to embed autonomous explorations into the general field from which it draws its ingredients. We then elaborate on the specific conceptual issues that arise in the context of autonomous computational procedures, some of which we discuss at an example catalytic system. [ABSTRACT FROM AUTHOR]

In homogeneous catalysis solvent is an inherent part of the catalytic system. As such, it must be considered in the computational modeling. The most common approach to include solvent effects in quantum mechanical calculations is by means of continuum solvent models. When they are properly used, average solvent effects are efficiently captured, mainly those related with solvent polarity. However, neglecting atomistic description of solvent molecules has its limitations, and continuum solvent models all alone cannot be applied to whatever situation. In many cases, inclusion of explicit solvent molecules in the quantum mechanical description of the system is mandatory. The purpose of this article is to highlight through selected examples what are the reasons that urge to go beyond the continuum models to the employment of micro-solvated (cluster-continuum) of fully explicit solvent models, in this way setting the limits of continuum solvent models in computational homogeneous catalysis. These examples showcase that inclusion of solvent molecules in the calculation not only can improve the description of already known mechanisms but can yield new mechanistic views of a reaction. With the aim of systematizing the use of explicit solvent models, after discussing the success and limitations of continuum solvent models, issues related with solvent coordination and solvent dynamics, solvent effects in reactions involving small, charged species, as well as reactions in protic solvents and the role of solvent as reagent itself are successively considered. [ABSTRACT FROM AUTHOR]