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The pharmacological space comprises all the dynamic events that determine the bioactivity (and/or the metabolism and toxicity) of a given ligand. The pharmacological space accounts for the structural flexibility and property variability of the two interacting molecules as well as for the mutual adaptability characterizing their molecular recognition process. The dynamic behavior of all these events can be described by a set of possible states (e.g., conformations, binding modes, isomeric forms) that the simulated systems can assume. For each monitored state, a set of state-dependent ligand- and structure-based descriptors can be calculated. Instead of considering only the most probable state (as routinely done), the pharmacological space proposes to consider all the monitored states. For each state-dependent descriptor, the corresponding space can be evaluated by calculating various dynamic parameters such as mean and range values.The reviewed examples emphasize that the pharmacological space can find fruitful applications in structure-based virtual screening as well as in toxicity prediction. In detail, in all reported examples, the inclusion of the pharmacological space parameters enhances the resulting performances. Beneficial effects are obtained by combining both different binding modes to account for ligand mobility and different target structures to account for protein flexibility/adaptability.The proposed computational workflow that combines docking simulations and rescoring analyses to enrich the arsenal of docking-based descriptors revealed a general applicability regardless of the considered target and utilized docking engine. Finally, the EFO approach that generates consensus models by linearly combining various descriptors yielded highly performing models in all discussed virtual screening campaigns.

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The discovery of molecular toxicity in a clinical drug candidate can have a significant impact on both the cost and timeline of the drug discovery process. Early identification of potentially toxic compounds during screening library preparation or, alternatively, during the hit validation process is critical to ensure that valuable time and resources are not spent pursuing compounds that may possess a high propensity for human toxicity. This report focuses on the application of computational molecular filters, applied either pre- or post-screening, to identify and remove known reactive and/or potentially toxic compounds from consideration in drug discovery campaigns.

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Machine learning (ML) has increasingly been applied to predict properties of drugs. Particularly, metabolism can be predicted with ML methods, which can be exploited during drug discovery and development. The prediction of metabolism is a crucial bottleneck in the early identification of toxic metabolites or biotransformation pathways that can affect elimination of the drug and potentially hinder the development of future new drugs. Metabolism prediction can be addressed with the application of ML models trained on large and validated dataset, from early stages of lead optimization to latest stage of drug development. ML methods rely on molecular descriptors that allow to identify and learn chemical and molecular features to predict sites of metabolism (SoMs) or activity associated with mechanism of inhibition (e.g., CYP inhibition). The application of ML methods in the prediction of drug metabolism represents a powerful resource to be exploited during drug discovery and development. ML allows to improve in silico screening and safety assessments of drugs in advance, steering their path to marketing authorization. Prediction of biotransformation reactions and metabolites allows to shorten the time, save the cost, and reduce animal testing. In this context, ML methods represent a technique to fill data gaps and an opportunity to reduce animal testing, calling for the 3R principles within the Big Data era.

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The Asclepios suite of KNIME nodes represents an innovative solution for conducting cheminformatics and computational chemistry tasks, specifically tailored for applications in drug discovery and computational toxicology. This suite has been developed using open-source and publicly accessible software. In this chapter, we introduce and explore the Asclepios suite through the lens of a case study. This case study revolves around investigating the interactions between per- and polyfluorinated alkyl substances (PFAS) and biomolecules, such as nuclear receptors. The objective is to characterize the potential toxicity of PFAS and gain insights into their chemical mode of action at the molecular level. The Asclepios KNIME nodes have been designed as versatile tools capable of addressing a wide range of computational toxicology challenges. Furthermore, they can be adapted and customized to accomodate the specific needs of individual users, spanning various domains such as nanoinformatics, biomedical research, and other related applications. This chapter provides an in-depth examination of the technical underpinnings and foundations of these tools. It is accompanied by a practical case study that demonstrates the utilization of Asclepios nodes in a computational toxicology investigation. This showcases the extendable functionalities that can be applied in diverse computational chemistry contexts. By the end of this chapter, we aim for readers to have a comprehensive understanding of the effectiveness of the Asclepios node functions. These functions hold significant potential for enhancing a wide spectrum of cheminformatics applications.

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Oligonucleotides, including DNA and RNA, can be functionalized by chemical modification based on synthetic organic chemistry. For example, ligand-oligonucleotide conjugates have a wide variety of applications. Conjugates of functional ligands and oligonucleotides have attracted attention in recent years as a drug delivery system (DDS) for improving the efficacy of oligonucleotide therapeutics. In addition, oligonucleotide conjugates with drug candidate compounds as ligands have been applied to drug screening using DNA-encoded libraries (DELs). Against this background, we have focused on the development of practical synthetic methods for ligand-oligonucleotide conjugates. Recently, we have developed a new synthetic method to construct oligonucleotides conjugated with coumarins and dipeptides, which are expected to have bioactivity, for application to DDS research of oligonucleotide therapeutics and drug discovery research using DEL. In this review, we will discuss the details, including how to construct a coumarin scaffold on oligonucleotides based on Knoevenagel condensation.

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OBJECTIVE: The coronavirus disease (COVID-19) pandemic, resulting from human-to-human transmission of a novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), has caused a global health emergency. The lack of a specific drug or treatment strategy against this disease makes it devastating. Given that the main protease (Mpro) of SARS-CoV-2 plays an indispensable role in viral polyprotein processing, its successful inhibition prevents viral replication and constrains virus spread. Therefore, developing an effective SARS-CoV-2 Mpro inhibitor to treat COVID-19 is imperative. MATERIALS AND METHODS: We employed a high-throughput screening (HTS) method based on fluorescence polarization (FP) assay and further confirmed by the fluorescence resonance energy transfer (FRET) method for the discovery of Mpro inhibitors. Then multiple approaches were taken to investigate the inhibition profiles of the hit compounds against Mpro, including 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) proliferation assay, surface plasmon resonance analysis (SPR), high-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry (HPLC-Q-TOF-MS), cytopathic effect (CPE) assay, molecule docking, and the drug-likeness analysis. RESULTS: In this study, four Mpro inhibitors with low toxicity were selected from HTS. According to SPR, all the hit compounds had medium binding affinities toward SARS-CoV-2 Mpro. HPLC-Q-TOF-MS results revealed the non-covalent linkage of each compound with SARS-CoV-2 Mpro. Molecule docking simulated the molecule interactions between each compound and the substrate binding pocket of SARS-CoV-2 Mpro. CPE assay was used to detect their inhibitory activities against coronaviruses HCoV-OC43 and HCoV-229E. In particular, the IMB63-8G compound demonstrated the highest antiviral potency [50% effective concentration (IC50) value of 1.71 µg/mL] and selectivity against HCoV-OC43 (SI = 39), which was more than 4-fold higher than that of ribavirin (RBV). Besides, the IMB63-8G compound possessed favorable drug-likeness characteristics. CONCLUSIONS: Our results will highlight the therapeutic potential of these compounds for the treatment of SARS-CoV-2 infection.

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Hepatic, biliary, and pancreatic cancer pose significant challenges in the field of digestive system diseases due to their highly malignant nature. Traditional Chinese medicine (TCM) has gained attention as a potential therapeutic approach with long-standing use in China and well-recognized clinical benefits. In this review, we systematically summarized the clinical applications of TCM that have shown promising results in clinical trials in treating hepatic, biliary, and pancreatic cancer. We highlighted several commonly used TCM therapeutics with validated efficacy through rigorous clinical trials, including Huaier Granule, Huachansu, and Icaritin. The active compounds and their potential targets have been thoroughly elucidated to offer valuable insights into the potential of TCM for anti-cancer drug discovery. We emphasized the importance of further research to bridge the gap between TCM and modern oncology, facilitating the development of evidence-based TCM treatment for these challenging malignancies.

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Decades-old project has new funding and a new set of compounds to test.

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Accurate prediction of molecular properties is crucial in drug discovery. Traditional methods often overlook that real-world molecules typically exhibit multiple property labels with complex correlations. To this end, we propose a novel framework, HiPM, which stands for Hierarchical Prompted Molecular representation learning framework. HiPM leverages task-aware prompts to enhance the differential expression of tasks in molecular representations and mitigate negative transfer caused by conflicts in individual task information. Our framework comprises two core components: the Molecular Representation Encoder (MRE) and the Task-Aware Prompter (TAP). MRE employs a hierarchical message-passing network architecture to capture molecular features at both the atom and motif levels. Meanwhile, TAP utilizes agglomerative hierarchical clustering algorithm to construct a prompt tree that reflects task affinity and distinctiveness, enabling the model to consider multi-granular correlation information among tasks, thereby effectively handling the complexity of multi-label property prediction. Extensive experiments demonstrate that HiPM achieves state-of-the-art performance across various multi-label datasets, offering a novel perspective on multi-label molecular representation learning.

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Alzheimer's disease (AD) is a significant global healthcare challenge, particularly in the elderly population. This neurodegenerative disorder is characterized by impaired memory and progressive decline in cognitive function. BACE1, a transmembrane protein found in neurons, oligodendrocytes, and astrocytes, exhibits varying levels across different neural subtypes. Abnormal BACE1 activity in the brains of individuals with AD leads to the formation of beta-amyloid proteins. The complex interplay between myelin sheath formation, BACE1 activity, and beta-amyloid accumulation suggests a critical role in understanding the pathological mechanisms of AD. The primary objective of this study was to identify molecular inhibitors that target Aß. Structure-based virtual screening (SBVS) was employed using the MCULE database, which houses over 2 million chemical compounds. A total of 59 molecules were selected after the toxicity profiling. Subsequently, five compounds conforming to the Egan-Egg permeation predictive model of the ADME rules were selected and subjected to molecular docking using AutoDock Vina on the Mcule drug discovery platform. The top two ligands and the positive control, 5HA, were subjected to molecular dynamics simulation for five nanoseconds. Toxicity profiling, physiochemical properties, lipophilicity, solubility, pharmacokinetics, druglikeness, medicinal chemistry attributes, average potential energy, RMSD, RMSF, and Rg analyses were conducted to identify the ligand MCULE-9199128437-0-2 as a promising inhibitor of BACE1.

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Drug discovery is a multi-disciplinary effort in which groups with expertise in a range of areas combine in a unified way to achieve a common goal: to deliver a clinical candidate to evaluate a hypothesis for improving human health. As a medicinal chemist this environment has provided multiple opportunities to be involved in cross-discipline interactions that have been both rewarding and led to outcomes that would not have been possible without an intimate interdisciplinary curiosity. Within this article I aim to share some of my experiences with the ß2-adrenoceptor that have fostered such synergistic relationships with several disciplines, but in particular with in vitro pharmacologists looking at different ways to stimulate this G protein-coupled receptor (GPCR). This interest now spans over a quarter of a century and has been intertwined with the delivery of three clinical candidates.

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Dual orexin receptor antagonists (DORAs) are approved for the treatment of sleep onset and/or sleep maintenance insomnia. In the present disclosure, we report the discovery of a new class of DORAs designed to treat sleep disorders requiring a fast onset and a short duration of action (<4 h). We used early human pharmacokinetic-pharmacodynamic (PK-PD) predictions and in vivo experiments to identify DORAs eliciting this specific hypnotic profile. A high-throughput screening campaign revealed hits based on a rarely precedented tricyclic pyrazolidine scaffold. After unsuccessful structure-activity-relationship (SAR) studies on this hit series, a scaffold hopping exercise, aimed at reducing the molecular complexity of the tricyclic scaffold, resulted in the discovery of the 2-acyl-1-biarylmethylpyrazolidine series. SAR studies on this achiral series gave rise to the lead compound DORA 42. In vitro and in vivo parameters of DORA 42, and its PK-PD simulation for human use are detailed.

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Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding pose and binding affinity predicted by computational docking. Here we develop a highly accurate structure-based virtual screen method, RosettaVS, for predicting docking poses and binding affinities. Our approach outperforms other state-of-the-art methods on a wide range of benchmarks, partially due to our ability to model receptor flexibility. We incorporate this into a new open-source artificial intelligence accelerated virtual screening platform for drug discovery. Using this platform, we screen multi-billion compound libraries against two unrelated targets, a ubiquitin ligase target KLHDC2 and the human voltage-gated sodium channel NaV1.7. For both targets, we discover hit compounds, including seven hits (14% hit rate) to KLHDC2 and four hits (44% hit rate) to NaV1.7, all with single digit micromolar binding affinities. Screening in both cases is completed in less than seven days. Finally, a high resolution X-ray crystallographic structure validates the predicted docking pose for the KLHDC2 ligand complex, demonstrating the effectiveness of our method in lead discovery.

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Small-molecule drug design hinges on obtaining co-crystallized ligand-protein structures. Despite AlphaFold2's strides in protein native structure prediction, its focus on apo structures overlooks ligands and associated holo structures. Moreover, designing selective drugs often benefits from the targeting of diverse metastable conformations. Therefore, direct application of AlphaFold2 models in virtual screening and drug discovery remains tentative. Here, we demonstrate an AlphaFold2-based framework combined with all-atom enhanced sampling molecular dynamics and Induced Fit docking, named AF2RAVE-Glide, to conduct computational model-based small-molecule binding of metastable protein kinase conformations, initiated from protein sequences. We demonstrate the AF2RAVE-Glide workflow on three different mammalian protein kinases and their type I and II inhibitors, with special emphasis on binding of known type II kinase inhibitors which target the metastable classical DFG-out state. These states are not easy to sample from AlphaFold2. Here, we demonstrate how with AF2RAVE these metastable conformations can be sampled for different kinases with high enough accuracy to enable subsequent docking of known type II kinase inhibitors with more than 50% success rates across docking calculations. We believe the protocol should be deployable for other kinases and more proteins generally.

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Peptides are valuable for therapeutic development, with multicyclic peptides showing promise in mimicking antigen-binding potency of antibodies. However, our capability to engineer multicyclic peptide scaffolds, particularly for the construction of large combinatorial libraries, is still limited. Here, we study the interplay of disulfide pairing between three biscysteine motifs, and designed a range of triscysteine motifs with unique disulfide-directing capability for regulating the oxidative folding of multicyclic peptides. We demonstrate that incorporating these motifs into random sequences allows the design of disulfide-directed multicyclic peptide (DDMP) libraries with up to four disulfide bonds, which have been applied for the successful discovery of peptide binders with nanomolar affinity to several challenging targets. This study encourages the use of more diverse disulfide-directing motifs for creating multicyclic peptide libraries and opens an avenue for discovering functional peptides in sequence and structural space beyond existing peptide scaffolds, potentially advancing the field of peptide drug discovery.

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Hit screening, which involves the identification of compounds or targets capable of modulating disease-relevant processes, is an important step in drug discovery. Some assays, such as image-based high-content screenings, produce complex multivariate readouts. To fully exploit the richness of such data, advanced analytical methods that go beyond the conventional univariate approaches should be employed. In this work, we tackle the problem of hit identification in multivariate assays. As with univariate assays, a hit from a multivariate assay can be defined as a candidate that yields an assay value sufficiently far away in distance from the mean or central value of inactives. Viewed another way, a hit is an outlier from the distribution of inactives. A method was developed for identifying multivariate hit in high-dimensional data sets based on principal components and robust Mahalanobis distance (the multivariate analogue to the Z- or T-statistic). The proposed method, termed mROUT (multivariate robust outlier detection), demonstrates superior performance over other techniques in the literature in terms of maintaining Type I error, false discovery rate and true discovery rate in simulation studies. The performance of mROUT is also illustrated on a CRISPR knockout data set from in-house phenotypic screening programme.

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To achieve malaria eradication, new preventative agents that act differently to front-line treatment drugs are needed. To identify potential chemoprevention starting points we screened a sub-set of the CSIRO Australia Compound Collection for compounds with slow-action in vitro activity against Plasmodium falciparum. This work identified N,N-dialkyl-5-alkylsulfonyl-1,3,4-oxadiazol-2-amines as a new antiplasmodial chemotype (e.g., 1 96 h IC50 550 nM; 3 96 h IC50 160 nM) with a different action to delayed-death slow-action drugs. A series of analogues were synthesized from thiotetrazoles and carbomoyl derivatives using Huisgen 1,3,4-oxadiazole synthesis followed by oxidation of the resultant thioethers to target sulfones. Structure activity relationship analysis of analogues identified compounds with potent and selective in vitro activity against drug-sensitive and multi-drug resistant Plasmodium parasites (e.g., 31 and 32 96 h IC50 <40 nM; SI > 2500). Subsequent studies in mice with compound 1, which had the best microsomal stability of the compounds assessed (T1/2 >255 min), demonstrated rapid clearance and poor oral in vivo efficacy in a P. berghei murine malaria model. These data indicate that while N,N-dialkyl-5-alkylsulfonyl-1,3,4-oxadiazol-2-amines are a novel class of slow-acting antiplasmodial agents, the further development of this chemotype for malaria chemoprophylaxis will require pharmacokinetic profile improvements.

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Chemical space is a multidimensional descriptor space that encloses all possible molecules, and at least 1 x 1060 organic substances with a molecular weight below 500 Da are thought to be potentially relevant for drug discovery. Natural products have been the primary source of the new pharmacological entities marketed during the past forty years and continue to be one of the most productive sources for the creation of innovative medications. Chemoinformatics-based computational tools accelerate the drug development process for natural products. Methods including estimating bioactivities, safety profiles, ADME, and natural product likeness measurement have been used. Here, we go over recent developments in chemoinformatic tools designed to visualize, characterize, and expand the chemical space of natural compound data sets using various molecular representations, create visual representations of such spaces, and investigate structure-property relationships within chemical spaces. With an emphasis on drug discovery applications, we evaluate the open-source databases BIOFACQUIM and PeruNPDB as proof of concept.

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Genomic technologies have led to massive gains in our understanding of human gene function and disease relevance. Chemical biologists are a primary beneficiary of this information, which can guide the prioritization of proteins for chemical probe and drug development. The vast functional and structural diversity of disease-relevant proteins, however, presents challenges for conventional small molecule screening libraries and assay development that in turn raise questions about the broader "druggability" of the human proteome. Here, we posit that activity-based protein profiling (ABPP), by generating global maps of small molecule-protein interactions in native biological systems, is well positioned to address major obstacles in human biology-guided chemical probe and drug discovery. We will support this viewpoint with case studies highlighting a range of small molecule mechanisms illuminated by ABPP that include the disruption and stabilization of biomolecular (protein-protein/nucleic acid) interactions and underscore allostery as a rich source of chemical tools for historically "undruggable" protein classes.

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