DOCKSTRING: Easy Molecular Docking Yields Better Benchmarks for Ligand Design.

The field of machine learning for drug discovery is witnessing an explosion of novel methods. These methods are often benchmarked on simple physicochemical properties such as solubility or general druglikeness, which can be readily computed. However, these properties are poor representatives of objective functions in drug design, mainly because they do not depend on the candidate compound's interaction with the target. By contrast, molecular docking is a widely applied method in drug discovery to estimate binding affinities. However, docking studies require a significant amount of domain knowledge to set up correctly, which hampers adoption. Here, we present dockstring, a bundle for meaningful and robust comparison of ML models using docking scores. dockstring consists of three components: (1) an open-source Python package for straightforward computation of docking scores, (2) an extensive dataset of docking scores and poses of more than 260,000 molecules for 58 medically relevant targets, and (3) a set of pharmaceutically relevant benchmark tasks such as virtual screening or de novo design of selective kinase inhibitors. The Python package implements a robust ligand and target preparation protocol that allows nonexperts to obtain meaningful docking scores. Our dataset is the first to include docking poses, as well as the first of its size that is a full matrix, thus facilitating experiments in multiobjective optimization and transfer learning. Overall, our results indicate that docking scores are a more realistic evaluation objective than simple physicochemical properties, yielding benchmark tasks that are more challenging and more closely related to real problems in drug discovery.

Systematic microsolvation approach with a cluster-continuum scheme and conformational sampling.

Solvation is a notoriously difficult and nagging problem for the rigorous theoretical description of chemistry in the liquid phase. Successes and failures of various approaches ranging from implicit solvation modeling through dielectric continuum embedding and microsolvated quantum chemical modeling to explicit molecular dynamics highlight this situation. Here, we focus on quantum chemical microsolvation and discuss an explicit conformational sampling ansatz to make this approach systematic. For this purpose, we introduce an algorithm for rolling and automated microsolvation of solutes. Our protocol takes conformational sampling and rearrangements in the solvent shell into account. Its reliability is assessed by monitoring the evolution of the spread and average of the observables of interest.

Exploration of Reaction Pathways and Chemical Transformation Networks.

For the investigation of chemical reaction networks, the identification of all relevant intermediates and elementary reactions is mandatory. Many algorithmic approaches exist that perform explorations efficiently and in an automated fashion. These approaches differ in their application range, the level of completeness of the exploration, and the amount of heuristics and human intervention required. Here, we describe and compare the different approaches based on these criteria. Future directions leveraging the strengths of chemical heuristics, human interaction, and physical rigor are discussed.

Error-Controlled Exploration of Chemical Reaction Networks with Gaussian Processes.

For a theoretical understanding of the reactivity of complex chemical systems, relative energies of stationary points on potential energy hypersurfaces need to be calculated to high accuracy. Due to the large number of intermediates present in all but the simplest chemical processes, approximate quantum chemical methods are required that allow for fast evaluations of the relative energies but at the expense of accuracy. Despite the plethora of benchmark studies, the accuracy of a quantum chemical method is often difficult to assess. Moreover, a significant improvement of a method's accuracy (e.g., through reparameterization or systematic model extension) is rarely possible. Here, we present a new approach that allows for the systematic, problem-oriented, and rolling improvement of quantum chemical results through the application of Gaussian processes. Due to its Bayesian nature, reliable error estimates are provided for each prediction. A reference method of high accuracy can be employed if the uncertainty associated with a particular calculation is above a given threshold. The new data point is then added to a growing data set in order to continuously improve the model and, as a result, all subsequent predictions. Previous predictions are validated by the updated model to ensure that uncertainties remain within the given confidence bound, which we call backtracking. We demonstrate our approach with the example of a complex chemical reaction network.

Context-Driven Exploration of Complex Chemical Reaction Networks.

The construction of a reaction network containing all relevant intermediates and elementary reactions is necessary for the accurate description of chemical processes. In the case of a complex chemical reaction (involving, for instance, many reactants or highly reactive species), the size of such a network may grow rapidly. Here, we present a computational protocol that constructs such reaction networks in a fully automated fashion steered in an intuitive, graph-based fashion through a single graphical user interface. Starting from a set of initial reagents new intermediates are explored through intra- and intermolecular reactions of already explored intermediates or new reactants presented to the network. This is done by assembling reactive complexes based on heuristic rules derived from conceptual electronic-structure theory and exploring the corresponding approximate reaction path. A subsequent path refinement leads to a minimum-energy path which connects the new intermediate to the existing ones to form a connected reaction network. Tree traversal algorithms are then employed to detect reaction channels and catalytic cycles. We apply our protocol to the formose reaction to study different pathways of sugar formation and to rationalize its autocatalytic nature.

Error Assessment of Computational Models in Chemistry.

Computational models in chemistry rely on a number of approximations. The effect of such approximations on observables derived from them is often unpredictable. Therefore, it is challenging to quantify the uncertainty of a computational result, which, however, is necessary to assess the suitability of a computational model. Common performance statistics such as the mean absolute error are prone to failure as they do not distinguish the explainable (systematic) part of the errors from their unexplainable (random) part. In this paper, we discuss problems and solutions for performance assessment of computational models based on several examples from the quantum chemistry literature. For this purpose, we elucidate the different sources of uncertainty, the elimination of systematic errors, and the combination of individual uncertainty components to the uncertainty of a prediction.

Uncertainty quantification for quantum chemical models of complex reaction networks.

For the quantitative understanding of complex chemical reaction mechanisms, it is, in general, necessary to accurately determine the corresponding free energy surface and to solve the resulting continuous-time reaction rate equations for a continuous state space. For a general (complex) reaction network, it is computationally hard to fulfill these two requirements. However, it is possible to approximately address these challenges in a physically consistent way. On the one hand, it may be sufficient to consider approximate free energies if a reliable uncertainty measure can be provided. On the other hand, a highly resolved time evolution may not be necessary to still determine quantitative fluxes in a reaction network if one is interested in specific time scales. In this paper, we present discrete-time kinetic simulations in discrete state space taking free energy uncertainties into account. The method builds upon thermo-chemical data obtained from electronic structure calculations in a condensed-phase model. Our kinetic approach supports the analysis of general reaction networks spanning multiple time scales, which is here demonstrated for the example of the formose reaction. An important application of our approach is the detection of regions in a reaction network which require further investigation, given the uncertainties introduced by both approximate electronic structure methods and kinetic models. Such cases can then be studied in greater detail with more sophisticated first-principles calculations and kinetic simulations.

The Harvard organic photovoltaic dataset.

The Harvard Organic Photovoltaic Dataset (HOPV15) presented in this work is a collation of experimental photovoltaic data from the literature, and corresponding quantum-chemical calculations performed over a range of conformers, each with quantum chemical results using a variety of density functionals and basis sets. It is anticipated that this dataset will be of use in both relating electronic structure calculations to experimental observations through the generation of calibration schemes, as well as for the creation of new semi-empirical methods and the benchmarking of current and future model chemistries for organic electronic applications.

Systematic Error Estimation for Chemical Reaction Energies.

For a theoretical understanding of the reactivity of complex chemical systems, accurate relative energies between intermediates and transition states are required. Despite its popularity, density functional theory (DFT) often fails to provide sufficiently accurate data, especially for molecules containing transition metals. Due to the huge number of intermediates that need to be studied for all but the simplest chemical processes, DFT is, to date, the only method that is computationally feasible. Here, we present a Bayesian framework for DFT that allows for error estimation of calculated properties. Since the optimal choice of parameters in present-day density functionals is strongly system dependent, we advocate for a system-focused reparameterization. While, at first sight, this approach conflicts with the first-principles character of DFT that should make it, in principle, system independent, we deliberately introduce system dependence to be able to assign a stochastically meaningful error to the system-dependent parametrization, which makes it nonarbitrary. By reparameterizing a functional that was derived on a sound physical basis to a chemical system of interest, we obtain a functional that yields reliable confidence intervals for reaction energies. We demonstrate our approach on the example of catalytic nitrogen fixation.

Heuristics-Guided Exploration of Reaction Mechanisms.

For the investigation of chemical reaction networks, the efficient and accurate determination of all relevant intermediates and elementary reactions is mandatory. The complexity of such a network may grow rapidly, in particular if reactive species are involved that might cause a myriad of side reactions. Without automation, a complete investigation of complex reaction mechanisms is tedious and possibly unfeasible. Therefore, only the expected dominant reaction paths of a chemical reaction network (e.g., a catalytic cycle or an enzymatic cascade) are usually explored in practice. Here, we present a computational protocol that constructs such networks in a parallelized and automated manner. Molecular structures of reactive complexes are generated based on heuristic rules derived from conceptual electronic-structure theory and subsequently optimized by quantum-chemical methods to produce stable intermediates of an emerging reaction network. Pairs of intermediates in this network that might be related by an elementary reaction according to some structural similarity measure are then automatically detected and subjected to an automated search for the connecting transition state. The results are visualized as an automatically generated network graph, from which a comprehensive picture of the mechanism of a complex chemical process can be obtained that greatly facilitates the analysis of the whole network. We apply our protocol to the Schrock dinitrogen-fixation catalyst to study alternative pathways of catalytic ammonia production.

Cluster headache and Alpha 1-antitrypsin deficiency.

Little is known about the pathophysiology of cluster headache (CH), one of the most debilitating primary headaches. Interestingly, associations of lung affecting diseases or lifestyle habits such as smoking and sleep apnoea syndrome and CH have been described. Certain genotypes for alpha 1-antitrypsin (alpha(1)-AT) are considered risk factors for emphysema. Our aim was to investigate possible associations between common genotypes of the SERPINA1 gene and CH. Our study included 55 CH patients and 55 controls. alpha(1)-AT levels in serum and the genotype were analysed. Patients CH characteristics were documented. We could not detect any association between CH and a genotype that does not match the homozygous wild type for alpha(1)-AT. Interestingly, there is a significant difference of CH attack frequency in patients who are heterozygous or homozygous M allele carriers. We conclude that the presence of an S or Z allele is associated with higher attack frequency in CH.

Molybdenum(VI) dioxo complexes with tridentate phenolate ligands.

A series of new molybdenum(VI) dioxo complexes of the type [MoO(2)ClL(X)] with potentially monoanionic phenolate ligands L(X) (L = 4,6-di-tert-butyl-2-{[(X)methylamino]methyl}-phenolate; L(OMe), where X = 2'-methoxyethyl; L(SEt), where X = 2'-ethylthioethyl; L(NEt2), where X = 2'-diethylaminoethyl; and L(NMe2), where X = 2'-dimethylaminoethyl) have been synthesized as models for molybdoenzymes. All molybdenum complexes were readily accessible by employing the eta(2)-coordinate pyrazolate complex [MoO(2)Cl(eta(2)-t-Bu(2)pz)]. The nitrogen ligand can easily be exchanged by the monoanionic phenolate ligands L(X) in toluene at room temperature, leading to monosubstituted complexes 1-4 as yellow to red powders in good yields. Suitable single crystals for X-ray diffraction analysis of complexes 2 and 3 were obtained from a concentrated benzene solution. Both complexes reveal a six-coordinate molybdenum atom in a distorted octahedral surrounding, with a tridentate fac coordination of the ligand. Additionally, the complexes were characterized by elemental analysis; IR, UV/vis, and NMR spectroscopy; and mass spectrometry. For complexes 1 and 2, only one isomer can be detected in solution, whereas complexes 3 and 4 reveal the formation of two isomers in a 1:1 ratio for 3 and in a 1:3 ratio for 4. Optimized geometries and relative free energies of all possible isomers have been established by DFT calculations, indicating two isomers in solutions of 3 and 4 to be two types of facially coordinated complexes. Furthermore, this is supported by gauge-independent atomic orbital calculations of the (1)H NMR shifts with a high correlation of experimental and calculated shifts for the fac compounds. Oxygen atom transfer reactions of 1 to PMe(3) quickly form monooxo molybdenum compound cis,mer-[MoOCl(2)(PMe(3))(3)].

Headache associated with sexual activity: prognosis and treatment options.

The aim of this study was to provide data on the prognosis and treatment options of headache associated with sexual activity (HSA). Sixty patients diagnosed with HSA between 1996 and 2004 were followed up between 2003 and 2006 at least 12 months after the first interview. The further course of the disease and their contentedness with therapy were requested. On average, the second interview was performed 35.9 months after the first examination. Of the 45 patients who had suffered from single attacks or bouts prior to baseline examination, 37 had no further attacks. Seven patients suffered from at least one further bout with an average duration of 2.1 months. One patient developed a chronic course of the disease after an episodic start. Of the 15 patients with chronic disease at the first examination, seven were in remission and five had ongoing attacks at follow-up. Ten patients received indomethacin for preemptive therapy, with good results in nine patients. Eighteen patients received beta-blockers for prophylaxis, with good results in 15 patients. Episodic HSA occurs in approximately three-quarters and chronic HSA in approximately one-quarter of patients. Even in chronic HAS, the prognosis is favourable, with remission rates of 69% during an observation period of 3 years. For patients with longer-lasting bouts or with chronic HSA, prophylactic treatment with beta-blockers or preemptive therapy with indomethacin are often successful.

[Anti-reflux surgery in patients with adequate response to proton pump inhibitors - is there an indication?]. / Antirefluxchirurgie bei patienten mit adäquatem ansprechen auf protonenpumpeninhibitoren - sinnvoll oder übertherapiert?

BACKGROUND: The indication for anti-reflux-surgery in patients with gastroesophageal reflux disease (GERD) who are totally symptom-free under conservative treatment with proton pump inhibitors (PPI) is still seen controversially. Arguments for PPI-treatment include the missing trauma and few side-effects, arguments against include the life-time-long drug intake. We prospectively observed the indication of anti-reflux-surgery in symptom-free patients in comparison to patients with remaining symptoms under treatment with PPI. PATIENTS AND METHODS: Between January 1999 and December 2001, 317 patients with GERD were treated by laparoscopic hemifundoplication and grouped in patients with adequate response to PPI (n = 103) and patients with residual discomfort in spite of or by PPI (n = 214). Preoperative diagnostic included endoscopy, ph-testing, manometry and quality of life scoring by a standardized questionnaire. Peri- and postoperative morbidity as well as quality of life were analyzed and compared with healthy individuals (n = 50). RESULTS: Patient demographics and surgical procedures did not differ between the two groups. After a median follow-up of 49 months and a follow-up rate of 89.9 %, the recurrence rate was 2.5 % and perioperative complication rate was 6.3 % without any significant differences between the groups. Patients with GERD and adequate response to PPI showed preoperatively during PPI omission a significant lower incidence of esophagitis (3.9 % vs. 18.2 %) and higher quality of life score (93.7 +/- 11.3 vs. 75.3 +/- 12.6), which significantly decreased in comparison to healthy individuals (132.9 +/- 10.5). After anti-reflux surgery the quality of life has been increased in both groups (130.5 +/- 11.4 vs. 121.8 +/- 13.2). However, only patients with adequate response to PPI reached the quality of life score of healthy individuals. CONCLUSION: Patients with preoperatively adequate response to PPI profit of anti-reflux surgery in terms of postoperative quality of life. Concerning the indication for anti-reflux surgery in these patients, one has to balance between the individual inconvenience due to the long-lasting drug intake on the one hand and the operative risk, morbidity and outcome of a specialized surgical department on the other hand.