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.

Apo2ph4: A Versatile Workflow for the Generation of Receptor-based Pharmacophore Models for Virtual Screening.

Pharmacophore models are widely used as efficient virtual screening (VS) filters for the target-directed enrichment of large compound libraries. However, the generation of pharmacophore models that have the power to discriminate between active and inactive molecules traditionally requires structural information about ligand-target complexes or at the very least knowledge of one active ligand. The fact that the discovery of the first known active ligand of a newly investigated target represents a major hurdle at the beginning of every drug discovery project underscores the need for methods that are able to derive high-quality pharmacophore models even without the prior knowledge of any active ligand structures. In this work, we introduce a novel workflow, called apo2ph4, that enables the rapid derivation of pharmacophore models solely from the three-dimensional structure of the target receptor. The utility of this workflow is demonstrated retrospectively for the generation of a pharmacophore model for the M2 muscarinic acetylcholine receptor. Furthermore, in order to show the general applicability of apo2ph4, the workflow was employed for all 15 targets of the recently published LIT-PCBA dataset. Pharmacophore-based VS runs using the apo2ph4-derived models achieved a significant enrichment of actives for 13 targets. In the last presented example, a pharmacophore model derived from the etomidate site of the α1ß2γ2 GABAA receptor was used in VS campaigns. Subsequent in vitro testing of selected hits revealed that 19 out of 20 (95%) tested compounds were able to significantly enhance GABA currents, which impressively demonstrates the applicability of apo2ph4 for real-world drug design projects.

High-Quality Conformer Generation with CONFORGE: Algorithm and Performance Assessment.

Knowledge of the putative bound-state conformation of a molecule is an essential prerequisite for the successful application of many computer-aided drug design methods that aim to assess or predict its capability to bind to a particular target receptor. An established approach to predict bioactive conformers in the absence of receptor structure information is to sample the low-energy conformational space of the investigated molecules and derive representative conformer ensembles that can be expected to comprise members closely resembling possible bound-state ligand conformations. The high relevance of such conformer generation functionality led to the development of a wide panel of dedicated commercial and open-source software tools throughout the last decades. Several published benchmarking studies have shown that open-source tools usually lag behind their commercial competitors in many key aspects. In this work, we introduce the open-source conformer ensemble generator CONFORGE, which aims at delivering state-of-the-art performance for all types of organic molecules in drug-like chemical space. The ability of CONFORGE and several well-known commercial and open-source conformer ensemble generators to reproduce experimental 3D structures as well as their computational efficiency and robustness has been assessed thoroughly for both typical drug-like molecules and macrocyclic structures. For small molecules, CONFORGE clearly outperformed all other tested open-source conformer generators and performed at least equally well as the evaluated commercial generators in terms of both processing speed and accuracy. In the case of macrocyclic structures, CONFORGE achieved the best average accuracy among all benchmarked generators, with RDKit's generator coming close in second place.

Identification of Promising Sulfonamide Chalcones as Inhibitors of SARS-CoV-2 3CLpro through Structure-Based Virtual Screening and Experimental Approaches.

3CLpro is a viable target for developing antiviral therapies against the coronavirus. With the urgent need to find new possible inhibitors, a structure-based virtual screening approach was developed. This study recognized 75 pharmacologically bioactive compounds from our in-house library of 1052 natural product-based compounds that satisfied drug-likeness criteria and exhibited good bioavailability and membrane permeability. Among these compounds, three promising sulfonamide chalcones were identified by combined theoretical and experimental approaches, with SWC423 being the most suitable representative compound due to its competitive inhibition and low cytotoxicity in Vero E6 cells (EC50 = 0.89 ± 0.32 µM; CC50 = 25.54 ± 1.38 µM; SI = 28.70). The binding and stability of SWC423 in the 3CLpro active site were investigated through all-atom molecular dynamics simulation and fragment molecular orbital calculation, indicating its potential as a 3CLpro inhibitor for further SARS-CoV-2 therapeutic research. These findings suggested that inhibiting 3CLpro with a sulfonamide chalcone such as SWC423 may pave the effective way for developing COVID-19 treatments.

GABAA receptor-mediated seizure liabilities: a mixed-methods screening approach.

GABAA receptors, members of the pentameric ligand-gated ion channel superfamily, are widely expressed in the central nervous system and mediate a broad range of pharmaco-toxicological effects including bidirectional changes to seizure threshold. Thus, detection of GABAA receptor-mediated seizure liabilities is a big, partly unmet need in early preclinical drug development. This is in part due to the plethora of allosteric binding sites that are present on different subtypes of GABAA receptors and the critical lack of screening methods that detect interactions with any of these sites. To improve in silico screening methods, we assembled an inventory of allosteric binding sites based on structural data. Pharmacophore models representing several of the binding sites were constructed. These models from the NeuroDeRisk IL Profiler were used for in silico screening of a compiled collection of drugs with known GABAA receptor interactions to generate testable hypotheses. Amoxapine was one of the hits identified and subjected to an array of in vitro assays to examine molecular and cellular effects on neuronal excitability and in vivo locomotor pattern changes in zebrafish larvae. An additional level of analysis for our compound collection is provided by pharmacovigilance alerts using FAERS data. Inspired by the Adverse Outcome Pathway framework, we postulate several candidate pathways leading from specific binding sites to acute seizure induction. The whole workflow can be utilized for any compound collection and should inform about GABAA receptor-mediated seizure risks more comprehensively compared to standard displacement screens, as it rests chiefly on functional data.

Pharmacokinetics of Novel Dopamine Transporter Inhibitor CE-123 and Modafinil with a Focus on Central Nervous System Distribution.

S-CE-123, a novel dopamine transporter inhibitor, has emerged as a potential candidate for cognitive enhancement. The objective of this study was to compare the tissue distribution profiles, with a specific focus on central nervous system distribution and metabolism, of S-CE-123 and R-modafinil. To address this objective, a precise liquid chromatography-high resolution mass spectrometry method was developed and partially validated. Neuropharmacokinetic parameters were assessed using the Combinatory Mapping Approach. Our findings reveal distinct differences between the two compounds. Notably, S-CE-123 demonstrates a significantly superior extent of transport across the blood-brain barrier (BBB), with an unbound brain-to-plasma concentration ratio (Kp,uu,brain) of 0.5, compared to R-modafinil's Kp,uu,brain of 0.1. A similar pattern was observed for the transport across the blood-spinal cord barrier. Concerning the drug transport across cellular membranes, we observed that S-CE-123 primarily localizes in the brain interstitial space, whereas R-modafinil distributes more evenly across both sides of the plasma membrane of the brain's parenchymal cells (Kp,uu,cell). Furthermore, our study highlights the substantial differences in hepatic metabolic stability, with S-CE-123 having a 9.3-fold faster metabolism compared to R-modafinil. In summary, the combination of improved BBB transport and higher affinity of S-CE-123 to dopamine transporters in comparison to R-modafinil makes S-CE-123 a promising candidate for further testing for the treatment of cognitive decline.

Reinstatement of synaptic plasticity in the aging brain through specific dopamine transporter inhibition.

Aging-related neurological deficits negatively impact mental health, productivity, and social interactions leading to a pronounced socioeconomic burden. Since declining brain dopamine signaling during aging is associated with the onset of neurological impairments, we produced a selective dopamine transporter (DAT) inhibitor to restore endogenous dopamine levels and improve cognitive function. We describe the synthesis and pharmacological profile of (S,S)-CE-158, a highly specific DAT inhibitor, which increases dopamine levels in brain regions associated with cognition. We find both a potentiation of neurotransmission and coincident restoration of dendritic spines in the dorsal hippocampus, indicative of reinstatement of dopamine-induced synaptic plasticity in aging rodents. Treatment with (S,S)-CE-158 significantly improved behavioral flexibility in scopolamine-compromised animals and increased the number of spontaneously active prefrontal cortical neurons, both in young and aging rodents. In addition, (S,S)-CE-158 restored learning and memory recall in aging rats comparable to their young performance in a hippocampus-dependent hole board test. In sum, we present a well-tolerated, highly selective DAT inhibitor that normalizes the age-related decline in cognitive function at a synaptic level through increased dopamine signaling.

In Silico and In Vitro Approach to Assess Direct Allosteric AMPK Activators from Nature.

The 5'-adenosine monophosphate-activated protein kinase (AMPK) is an important metabolic regulator. Its allosteric drug and metabolite binding (ADaM) site was identified as an attractive target for direct AMPK activation and holds promise as a novel mechanism for the treatment of metabolic diseases. With the exception of lusianthridin and salicylic acid, no natural product (NP) is reported so far to directly target the ADaM site. For the streamlined assessment of direct AMPK activators from the pool of NPs, an integrated workflow using in silico and in vitro methods was applied. Virtual screening combining a 3D shape-based approach and docking identified 21 NPs and NP-like molecules that could potentially activate AMPK. The compounds were purchased and tested in an in vitro AMPK α 1 ß 1 γ 1 kinase assay. Two NP-like virtual hits were identified, which, at 30 µM concentration, caused a 1.65-fold (± 0.24) and a 1.58-fold (± 0.17) activation of AMPK, respectively. Intriguingly, using two different evaluation methods, we could not confirm the bioactivity of the supposed AMPK activator lusianthridin, which rebuts earlier reports.

Novel Class of Proteasome Inhibitors: In Silico and In Vitro Evaluation of Diverse Chloro(trifluoromethyl)aziridines.

The ubiquitin-proteasome pathway (UPP) is the major proteolytic system in the cytosol and nucleus of all eukaryotic cells. The role of proteasome inhibitors (PIs) as critical agents for regulating cancer cell death has been established. Aziridine derivatives are well-known alkylating agents employed against cancer. However, to the best of our knowledge, aziridine derivatives showing inhibitory activity towards proteasome have never been described before. Herein we report a new class of selective and nonPIs bearing an aziridine ring as a core structure. In vitro cell-based assays (two leukemia cell lines) also displayed anti-proliferative activity for some compounds. In silico studies indicated non-covalent binding mode and drug-likeness for these derivatives. Taken together, these results are promising for developing more potent PIs.

Synthesis, Biological Evaluation, and Docking Studies of Antagonistic Hydroxylated Arecaidine Esters Targeting mAChRs.

The muscarinic acetylcholine receptor family is a highly sought-after target in drug and molecular imaging discovery efforts aimed at neurological disorders. Hampered by the structural similarity of the five subtypes' orthosteric binding pockets, these efforts largely failed to deliver subtype-selective ligands. Building on our recent successes with arecaidine-derived ligands targeting M1, herein we report the synthesis of a related series of 11 hydroxylated arecaidine esters. Their physicochemical property profiles, expressed in terms of their computationally calculated CNS MPO scores and HPLC-logD values, point towards blood-brain barrier permeability. By means of a competitive radioligand binding assay, the binding affinity values towards each of the individual human mAChR subtypes hM1-hM5 were determined. The most promising compound of this series 17b was shown to have a binding constant towards hM1 in the single-digit nanomolar region (5.5 nM). Similar to our previously reported arecaidine-derived esters, the entire series was shown to act as hM1R antagonists in a calcium flux assay. Overall, this study greatly expanded our understanding of this recurring scaffolds' structure-activity relationship and will guide the development towards highly selective mAChRs ligands.

Structural Insights into the Mechanisms of Action of Functionally Distinct Classes of Chikungunya Virus Nonstructural Protein 1 Inhibitors.

Chikungunya virus (CHIKV) nonstructural protein 1 (nsP1) harbors the methyltransferase (MTase) and guanylyltransferase (GTase) activities needed for viral RNA capping and represents a promising antiviral drug target. We compared the antiviral efficacies of nsP1 inhibitors belonging to the MADTP, CHVB, and FHNA series (6'-fluoro-homoneplanocin A [FHNA], its 3'-keto form, and 6'-ß-fluoro-homoaristeromycin). Cell-based phenotypic cross-resistance assays revealed that the CHVB and MADTP series had similar modes of action that differed from that of the FHNA series. In biochemical assays with purified Semliki Forest virus and CHIKV nsP1, CHVB compounds strongly inhibited MTase and GTase activities, while MADTP-372 had a moderate inhibitory effect. FHNA did not directly inhibit the enzymatic activity of CHIKV nsP1. The first-of-their-kind molecular-docking studies with the cryo-electron microscopy (cryo-EM) structure of CHIKV nsP1, which is assembled into a dodecameric ring, revealed that the MADTP and CHVB series bind at the S-adenosylmethionine (SAM)-binding site in the capping domain, where they would function as competitive or noncompetitive inhibitors. The FHNA series was predicted to bind at the secondary binding pocket in the ring-aperture membrane-binding and oligomerization (RAMBO) domain, potentially interfering with the membrane binding and oligomerization of nsP1. Our cell-based and enzymatic assays, in combination with molecular docking and mapping of compound resistance mutations to the nsP1 structure, allowed us to group nsP1 inhibitors into functionally distinct classes. This study identified druggable pockets in the nsP1 dodecameric structure and provides a basis for the rational design, optimization, and combination of inhibitors of this unique and promising antiviral drug target.

Support Vector Machine as a Supervised Learning for the Prioritization of Novel Potential SARS-CoV-2 Main Protease Inhibitors.

In the last year, the COVID-19 pandemic has highly affected the lifestyle of the world population, encouraging the scientific community towards a great effort on studying the infection molecular mechanisms. Several vaccine formulations are nowadays available and helping to reach immunity. Nevertheless, there is a growing interest towards the development of novel anti-covid drugs. In this scenario, the main protease (Mpro) represents an appealing target, being the enzyme responsible for the cleavage of polypeptides during the viral genome transcription. With the aim of sharing new insights for the design of novel Mpro inhibitors, our research group developed a machine learning approach using the support vector machine (SVM) classification. Starting from a dataset of two million commercially available compounds, the model was able to classify two hundred novel chemo-types as potentially active against the viral protease. The compounds labelled as actives by SVM were next evaluated through consensus docking studies on two PDB structures and their binding mode was compared to well-known protease inhibitors. The best five compounds selected by consensus docking were then submitted to molecular dynamics to deepen binding interactions stability. Of note, the compounds selected via SVM retrieved all the most important interactions known in the literature.

In Silico Identification of Potential Druggable Binding Sites on CIN85 SH3 Domain.

Protein-protein interactions (PPIs) play a pivotal role in the regulation of many physiological processes. The dysfunction of some PPIs interactions led to the alteration of different biological pathways causing various diseases including cancer. In this context, the inhibition of PPIs represents an attractive strategy for the design of new antitumoral agents. In recent years, computational approaches were successfully used to study the interactions between proteins, providing useful hints for the design of small molecules able to modulate PPIs. Targeting PPIs presents several challenges mainly due to the large and flat binding surface that lack the typical binding pockets of traditional drug targets. Despite these hurdles, substantial progress has been made in the last decade resulting in the identification of PPI modulators where some of them even found clinical use. This study focuses on MUC1-CIN85 PPI which is involved in the migration and invasion of cancer cells. Particularly, we investigated the presence of druggable binding sites on the CIN85 surface which provided new insights for the structure-based design of novel MUC1-CIN85 PPI inhibitors as anti-metastatic agents.

Greedy 3-Point Search (G3PS)-A Novel Algorithm for Pharmacophore Alignment.

Chemical features of small molecules can be abstracted to 3D pharmacophore models, which are easy to generate, interpret, and adapt by medicinal chemists. Three-dimensional pharmacophores can be used to efficiently match and align molecules according to their chemical feature pattern, which facilitates the virtual screening of even large compound databases. Existing alignment methods, used in computational drug discovery and bio-activity prediction, are often not suitable for finding matches between pharmacophores accurately as they purely aim to minimize RMSD or maximize volume overlap, when the actual goal is to match as many features as possible within the positional tolerances of the pharmacophore features. As a consequence, the obtained alignment results are often suboptimal in terms of the number of geometrically matched feature pairs, which increases the false-negative rate, thus negatively affecting the outcome of virtual screening experiments. We addressed this issue by introducing a new alignment algorithm, Greedy 3-Point Search (G3PS), which aims at finding optimal alignments by using a matching-feature-pair maximizing search strategy while at the same time being faster than competing methods.

Improved Lipophilicity and Aqueous Solubility Prediction with Composite Graph Neural Networks.

The accurate prediction of molecular properties, such as lipophilicity and aqueous solubility, are of great importance and pose challenges in several stages of the drug discovery pipeline. Machine learning methods, such as graph-based neural networks (GNNs), have shown exceptionally good performance in predicting these properties. In this work, we introduce a novel GNN architecture, called directed edge graph isomorphism network (D-GIN). It is composed of two distinct sub-architectures (D-MPNN, GIN) and achieves an improvement in accuracy over its sub-architectures employing various learning, and featurization strategies. We argue that combining models with different key aspects help make graph neural networks deeper and simultaneously increase their predictive power. Furthermore, we address current limitations in assessment of deep-learning models, namely, comparison of single training run performance metrics, and offer a more robust solution.

Consecutive and Selective Double Methylene Insertion of Lithium Carbenoids to Isothiocyanates: A Direct Assembly of Four-Membered Sulfur-Containing Cycles.

A formal CH2 -CH2 homologation conducted with C1 carbenoids on a carbon electrophile for the obtainment of a four-membered cycle is reported. The logic proposes the consecutive delivery of two single nucleophilic CH2 units to an isothiocyanate-as competent electrophilic partner-resulting in the assembling of a rare imino-thietane cluster. The single synthetic operation procedure documents genuine chemocontrol, as indicated by the tolerance to various reactive elements decorating the starting materials. Significantly, the double homologation protocol is accomplished directly on a carbon electrophile, thus not requiring the installation of heteroatom-centered manifolds (e.g. boron).

Novel Class of Chikungunya Virus Small Molecule Inhibitors That Targets the Viral Capping Machinery.

Despite the worldwide reemergence of the chikungunya virus (CHIKV) and the high morbidity associated with CHIKV infections, there is no approved vaccine or antiviral treatment available. Here, we aimed to identify the target of a novel class of CHIKV inhibitors, i.e., the CHVB series. CHVB compounds inhibit the in vitro replication of CHIKV isolates with 50% effective concentrations in the low-micromolar range. A CHVB-resistant variant (CHVBres) was selected that carried two mutations in the gene encoding nsP1 (responsible for viral RNA capping), one mutation in nsP2, and one mutation in nsP3. Reverse genetics studies demonstrated that both nsP1 mutations were necessary and sufficient to achieve ∼18-fold resistance, suggesting that CHVB targets viral mRNA capping. Interestingly, CHVBres was cross-resistant to the previously described CHIKV capping inhibitors from the MADTP series, suggesting they share a similar mechanism of action. In enzymatic assays, CHVB inhibited the methyltransferase and guanylyltransferase activities of alphavirus nsP1 proteins. To conclude, we identified a class of CHIKV inhibitors that targets the viral capping machinery. The potent anti-CHIKV activity makes this chemical scaffold a potential candidate for CHIKV drug development.

A-ring and E-ring modifications of the cytotoxic alkaloid Luotonin A: Synthesis, computational and biological studies.

A series of new Luotonin A derivatives with substituents at rings A and E was synthesized, together with some E-ring-unsubstituted derivatives. Subsequently, the compound library was examined in silico for their binding into a previously proposed site in the DNA/topoisomerase I binary complex. Whereas no convincing correlation between docking scores and biological data from in vitro assays could be found, one novel 4,9-diamino Luotonin A derivative had strong antiproliferative activity based on massive G2/M phase arrest. As this biological activity clearly differs from the reference compound Camptothecin, this strongly indicates that at least some Luotonin A derivatives may be potent antiproliferative agents, however with a different mode of action.

A compact review of molecular property prediction with graph neural networks.

As graph neural networks are becoming more and more powerful and useful in the field of drug discovery, many pharmaceutical companies are getting interested in utilizing these methods for their own in-house frameworks. This is especially compelling for tasks such as the prediction of molecular properties which is often one of the most crucial tasks in computer-aided drug discovery workflows. The immense hype surrounding these kinds of algorithms has led to the development of many different types of promising architectures and in this review we try to structure this highly dynamic field of AI-research by collecting and classifying 80 GNNs that have been used to predict more than 20 molecular properties using 48 different datasets.

A Novel Series of [1,2,4]Triazolo[4,3-a]Pyridine Sulfonamides as Potential Antimalarial Agents: In Silico Studies, Synthesis and In Vitro Evaluation.

For the development of new and potent antimalarial drugs, we designed the virtual library with three points of randomization of novel [1,2,4]triazolo[4,3-a]pyridines bearing a sulfonamide fragment. The library of 1561 compounds has been investigated by both virtual screening and molecular docking methods using falcipain-2 as a target enzyme. 25 chosen hits were synthesized and evaluated for their antimalarial activity in vitro against Plasmodium falciparum. 3-Ethyl-N-(3-fluorobenzyl)-N-(4-methoxyphenyl)-[1,2,4]triazolo[4,3-a]pyridine-6-sulfonamide and 2-(3-chlorobenzyl)-8-(piperidin-1-ylsulfonyl)-[1,2,4]triazolo[4,3-a]pyridin-3(2H)-one showed in vitro good antimalarial activity with inhibitory concentration IC50 = 2.24 and 4.98 µM, respectively. This new series of compounds may serve as a starting point for future antimalarial drug discovery programs.