Active Learning Approach for Guiding Site-of-Metabolism Measurement and Annotation.

The ability to determine and predict metabolically labile atom positions in a molecule (also called "sites of metabolism" or "SoMs") is of high interest to the design and optimization of bioactive compounds, such as drugs, agrochemicals, and cosmetics. In recent years, several in silico models for SoM prediction have become available, many of which include a machine-learning component. The bottleneck in advancing these approaches is the coverage of distinct atom environments and rare and complex biotransformation events with high-quality experimental data. Pharmaceutical companies typically have measured metabolism data available for several hundred to several thousand compounds. However, even for metabolism experts, interpreting these data and assigning SoMs are challenging and time-consuming. Therefore, a significant proportion of the potential of the existing metabolism data, particularly in machine learning, remains dormant. Here, we report on the development and validation of an active learning approach that identifies the most informative atoms across molecular data sets for SoM annotation. The active learning approach, built on a highly efficient reimplementation of SoM predictor FAME 3, enables experts to prioritize their SoM experimental measurements and annotation efforts on the most rewarding atom environments. We show that this active learning approach yields competitive SoM predictors while requiring the annotation of only 20% of the atom positions required by FAME 3. The source code of the approach presented in this work is publicly available.

Process Development and Scale-Up of a Novel Atypical DAT Inhibitor (S)-CE-123.

Large-scale syntheses of small molecules and kilo laboratories are crucial steps in drug development, especially in advanced stages. (S)-5-((Benzhydrylsulfinyl)methyl)thiazole, (S)-CE-123, a potent, selective, and novel atypical DAT inhibitor, has undergone iterative testing as part of the preclinical evaluation step. This required the process transfer, scale-up, and synthesis of a 1 kg preclinical batch. The Kagan protocol for asymmetric sulfide to sulfoxide oxidation was successfully applied within a four-step synthetic process for the successful upscaling of (S)-CE-123. During the scale-up of the last step, several changes were made to the original synthetic procedure, as with every increase in batch size, new problems had to be overcome. These include, among others, the workup optimization of the last step, the simplification of chromatographic purification, elution modification to improve the purity of the product and saving of workup time. Two washing steps were added to the original procedure to enhance both the yield and the enantiomeric excess value of the final product. The modifications introduced allowed access to a 1 kg (S)-CE-123 batch with a purity >99% and an enantiomeric excess value of 95%.

Reweighting from Molecular Mechanics Force Fields to the ANI-2x Neural Network Potential.

To achieve chemical accuracy in free energy calculations, it is necessary to accurately describe the system's potential energy surface and efficiently sample configurations from its Boltzmann distribution. While neural network potentials (NNPs) have shown significantly higher accuracy than classical molecular mechanics (MM) force fields, they have a limited range of applicability and are considerably slower than MM potentials, often by orders of magnitude. To address this challenge, Rufa et al. [Rufa et al. bioRxiv 2020, 10.1101/2020.07.29.227959.] suggested a two-stage approach that uses a fast and established MM alchemical energy protocol, followed by reweighting the results using NNPs, known as endstate correction or indirect free energy calculation. This study systematically investigates the accuracy and robustness of reweighting from an MM reference to a neural network target potential (ANI-2x) for an established data set in vacuum, using single-step free-energy perturbation (FEP) and nonequilibrium (NEQ) switching simulation. We assess the influence of longer switching lengths and the impact of slow degrees of freedom on outliers in the work distribution and compare the results to those of multistate equilibrium free energy simulations. Our results demonstrate that free energy calculations between NNPs and MM potentials should be preferably performed using NEQ switching simulations to obtain accurate free energy estimates. NEQ switching simulations between the MM potentials and NNPs are efficient, robust, and trivial to implement.

Investigating experimental vs. Predicted pKa values for PET radiotracer.

The prediction of central nervous system (CNS) active pharmaceuticals and radiopharmaceuticals has experienced a boost by the introduction of computational approaches, like blood-brain barrier (BBB) score or CNS multiparameter optimization values. These rely heavily on calculated pKa values and other physicochemical parameters. Despite the inclusion of various physicochemical parameters in online data banks, pKa values are often missing and published experimental pKa values are limited especially for radiopharmaceuticals. This comparative study investigated the discrepancies between predicted and experimental pKa values and their impact on CNS activity prediction scores. The pKa values of 46 substances, including therapeutic drugs and PET imaging radiopharmaceuticals, were measured by means of potentiometry and spectrophotometry. Experimentally obtained pKa values were compared with in silico predictions (Chemicalize/Marvin). The results demonstrate a considerable discrepancy between experimental and in silico values, with linear regression analysis showing intermediate correlation (R2(Marvin) = 0.88, R2(Chemicalize) = 0.82). This indicates that if one requires an accurate pKa value, it is essential to experimentally assess it. This underscores the importance of experimentally determining pKa values for accurate drug design and optimization. The study's data provide a valuable library of reliable experimental pKa values for therapeutic drugs and radiopharmaceuticals, aiding researchers in the field.

Insights and Challenges in Correcting Force Field Based Solvation Free Energies Using a Neural Network Potential.

We present a comprehensive study investigating the potential gain in accuracy for calculating absolute solvation free energies (ASFE) using a neural network potential to describe the intramolecular energy of the solute. We calculated the ASFE for most compounds from the FreeSolv database using the Open Force Field (OpenFF) and compared them to earlier results obtained with the CHARMM General Force Field (CGenFF). By applying a nonequilibrium (NEQ) switching approach between the molecular mechanics (MM) description (either OpenFF or CGenFF) and the neural net potential (NNP)/MM level of theory (using ANI-2x as the NNP potential), we attempted to improve the accuracy of the calculated ASFEs. The predictive performance of the results did not change when this approach was applied to all 589 small molecules in the FreeSolv database that ANI-2x can describe. When selecting a subset of 156 molecules, focusing on compounds where the force fields performed poorly, we saw a slight improvement in the root-mean-square error (RMSE) and mean absolute error (MAE). The majority of our calculations utilized unidirectional NEQ protocols based on Jarzynski's equation. Additionally, we conducted bidirectional NEQ switching for a subset of 156 solutes. Notably, only a small fraction (10 out of 156) exhibited statistically significant discrepancies between unidirectional and bidirectional NEQ switching free energy estimates.

Design, synthesis, and lead optimization of piperazinyl-pyrimidine analogues as potent small molecules targeting the viral capping machinery of Chikungunya virus.

The worldwide re-emerge of the Chikungunya virus (CHIKV), the high morbidity associated with it, and the lack of an available vaccine or antiviral treatment make the development of a potent CHIKV-inhibitor highly desirable. Therefore, an extensive lead optimization was performed based on the previously reported CHVB compound 1b and the reported synthesis route was optimized - improving the overall yield in remarkably shorter synthesis and work-up time. Hundred analogues were designed, synthesized, and investigated for their antiviral activity, physiochemistry, and toxicological profile. An extensive structure-activity relationship study (SAR) was performed, which focused mainly on the combination of scaffold changes and revealed the key chemical features for potent anti-CHIKV inhibition. Further, a thorough ADMET investigation of the compounds was carried out: the compounds were screened for their aqueous solubility, lipophilicity, their toxicity in CaCo-2 cells, and possible hERG channel interactions. Additionally, 55 analogues were assessed for their metabolic stability in human liver microsomes (HLMs), leading to a structure-metabolism relationship study (SMR). The compounds showed an excellent safety profile, favourable physicochemical characteristics, and the required metabolic stability. A cross-resistance study confirmed the viral capping machinery (nsP1) to be the viral target of these compounds. This study identified 31b and 34 as potent, safe, and stable lead compounds for further development as selective CHIKV inhibitors. Finally, the collected insight led to a successful scaffold hop (64b) for future antiviral research studies.