Search

In a unique collaboration between Simulations Plus and several industrial partners, we were able to develop a new version 11.0 of the previously published in silico pKa model, S+pKa, with considerably improved prediction accuracy. The model's training set was vastly expanded by large amounts of experimental data obtained from F. Hoffmann‐La Roche AG, Genentech Inc., and the Crop Science division of Bayer AG. The previous v7.0 of S+pKa was trained on data from public sources and the Pharmaceutical division of Bayer AG. The model has shown dramatic improvements in predictive accuracy when externally validated on three new contributor compound sets. Less expected was v11.0's improvement in prediction on new compounds developed at Bayer Pharma after v7.0 was released (2013–2023), even without contributing additional data to v11.0. We illustrate chemical space coverage by chemistries encountered in the five domains, public and industrial, outline model construction, and discuss factors contributing to model's success. [ABSTRACT FROM AUTHOR]

The resonance Raman (RR) spectra of iron complexes containing the Fe2(mu-O)2 diamond core using Fe, O, and N isotope substitution and ligand modification is analyzed. Using the available isotopic substitution data, a normal coordinate analysis for the Fe2(mu-O)2 core was carried out in (Fe2(mu-O)2(L)2)3+ (L = TPA and its methyl-substituted derivatives). The nature of the unique vibration was assigned in the 600-700 cm/1 region that is the Raman signature for the diamond core type.

A normal coordinate analysis of the resonance Raman spectra of the CuA fragment from the cytochrome c oxidase of Paracoccus denitrificans was conducted with CuA replaced by isotopes of Cu, S and N. The Cu- and N-isotope dependencies agree with the view that the vibrational motions in CuA are mostly caused by the Cu and S atoms with much less coupling to the vibrations of the cysteinate side chain and polypeptide backbone than in the mononuclear copper-cysteinate proteins.

Resonance Raman (RR) spectroscopic analysis of oxyhemocyanin from arthropods and mollusks reveals that hemocyanins (Hc) from both phyla have similar mu-eta(2):eta(2) copper peroxide structure. Labeling studies show the equivalence of the two oxygen atoms. Observation of a single vibration in the RR spectrum of a hemocyanin (HC) that contains labeled oxygen atoms indicates the symmetric binding between copper ions and peroxide. Normal coordination analysis, 63/65 Cu and deuterium isotopic studies show that Hcs from both phyla have identical sets of terminal copper ligands.

Presents a study on the resonance Raman (RR) spectra of iron complexes containing the Fe2O2 `diamond' core. Results and discussion on the study; Detailed information on experiments conducted; More indepth look at the study.

In a unique collaboration between Simulations Plus and several industrial partners, we were able to develop a new version 11.0 of the previously published in silico pK a model, S+pKa, with considerably improved prediction accuracy. The model's training set was vastly expanded by large amounts of experimental data obtained from F. Hoffmann-La Roche AG, Genentech Inc., and the Crop Science division of Bayer AG. The previous v7.0 of S+pKa was trained on data from public sources and the Pharmaceutical division of Bayer AG. The model has shown dramatic improvements in predictive accuracy when externally validated on three new contributor compound sets. Less expected was v11.0's improvement in prediction on new compounds developed at Bayer Pharma after v7.0 was released (2013-2023), even without contributing additional data to v11.0. We illustrate chemical space coverage by chemistries encountered in the five domains, public and industrial, outline model construction, and discuss factors contributing to model's success., (© 2024 The Author(s). Molecular Informatics published by Wiley-VCH GmbH.)

In this work, we show that the apparent p K a measured by standard titration experiments is an insufficient measure of acidity or basicity of organic functional groups in multiprotic compounds─a frequent aspect of lead optimization in pharmaceutical research. We show that the use of the apparent p K a in this context may result in costly mistakes. To properly represent the group's true acidity/basicity, we propose p K 50 ─a single-proton midpoint measure derived from a statistical thermodynamics treatment of multiprotic ionization. We show that p K 50 , which may be directly measured in specialized NMR titration experiments, is superior in tracking the functional group's acidity/basicity across congeneric series of related compounds and converges to the well familiar ionization constant in the monoprotic case.

Drug discovery and development is a costly and time-consuming endeavor (Calcoen et al. Nat Rev Drug Discov 14(3):161-162, 2015; The truly staggering cost of inventing new drugs. Forbes. http://www.forbes.com/sites/matthewherper/2012/02/10/the-truly-staggering-cost-of-inventing-new-drugs/, 2012; Scannell et al. Nat Rev Drug Discov 11(3):191-200, 2012). Over the last two decades, computational tools and in silico models to predict ADMET (Adsorption, Distribution, Metabolism, Excretion, and Toxicity) profiles of molecules have been incorporated into the drug discovery process mainly in an effort to avoid late-stage failures due to poor pharmacokinetics and toxicity. It is now widely recognized that ADMET issues should be addressed as early as possible in drug discovery. Here, we describe in detail how ADMET models can be developed and applied using a commercially available package, ADMET Predictor™ 7.2 (ADMET Predictor v7.2. Simulations Plus, Inc., Lancaster, CA, USA).

In a unique collaboration between a software company and a pharmaceutical company, we were able to develop a new in silico pKa prediction tool with outstanding prediction quality. An existing pKa prediction method from Simulations Plus based on artificial neural network ensembles (ANNE), microstates analysis, and literature data was retrained with a large homogeneous data set of drug-like molecules from Bayer. The new model was thus built with curated sets of ∼14,000 literature pKa values (∼11,000 compounds, representing literature chemical space) and ∼19,500 pKa values experimentally determined at Bayer Pharma (∼16,000 compounds, representing industry chemical space). Model validation was performed with several test sets consisting of a total of ∼31,000 new pKa values measured at Bayer. For the largest and most difficult test set with >16,000 pKa values that were not used for training, the original model achieved a mean absolute error (MAE) of 0.72, root-mean-square error (RMSE) of 0.94, and squared correlation coefficient (R(2)) of 0.87. The new model achieves significantly improved prediction statistics, with MAE = 0.50, RMSE = 0.67, and R(2) = 0.93. It is commercially available as part of the Simulations Plus ADMET Predictor release 7.0. Good predictions are only of value when delivered effectively to those who can use them. The new pKa prediction model has been integrated into Pipeline Pilot and the PharmacophorInformatics (PIx) platform used by scientists at Bayer Pharma. Different output formats allow customized application by medicinal chemists, physical chemists, and computational chemists.