Drug Discovery Process Research Articles

Improvements in Precision of Relative Binding Free Energy Calculations Afforded by the Alchemical Enhanced Sampling (ACES) Approach.

Accurate in silico predictions of how strongly small molecules bind to proteins, such as those afforded by relative binding free energy (RBFE) calculations, can greatly increase the efficiency of the hit-to-lead and lead optimization stages of the drug discovery process. The success of such calculations, however, relies heavily on their precision. Here, we show that a recently developed alchemical enhanced sampling (ACES) approach can consistently improve the precision of RBFE calculations on a large and diverse set of proteins and small molecule ligands. The addition of ACES to conventional RBFE calculations lowered the average hysteresis by over 35% (0.3-0.4 kcal/mol) and the average replicate spread by over 25% (0.2-0.3 kcal/mol) across a set of 10 protein targets and 213 small molecules while maintaining similar or improved accuracy. We show in atomic detail how ACES improved convergence of several representative RBFE calculations through enhancing the sampling of important slowly transitioning ligand degrees of freedom.

Repurposing of a library for high-content screening of inhibitors against Echinococcus granulosus

BackgroundCystic echinococcosis (CE) is a zoonotic disease caused by the larval stage of the dog tapeworm Echinococcus granulosus sensu lato (E. granulosus), with a worldwide distribution. The current treatment strategy for CE is insufficient. Limited drug screening models severely hamper the discovery of effective anti-echinococcosis drugs.MethodsIn the present study, using high-content screening technology, we developed a novel high-throughput screening (HTS) assay by counting the ratio of propidium iodide-stained dead protoscoleces (PSCs) to the total number of PSCs. In vitro and ex vivo cyst viability assays were utilized to determine the effect of drugs on cyst viability.ResultsUsing the newly established HTS assay, we screened approximately 12,000 clinical-stage or The Food and Drug Administration (FDA)-approved small molecules from the Repurposing, Focused Rescue, and Accelerated Medchem (ReFRAME) library, as well as the LOPAC1280 and SelleckChem libraries, as a strategic approach to facilitate the drug discovery process. Initial screening yielded 173 compounds with anti-echinococcal properties, 52 of which demonstrated dose–response efficacy against E. granulosus PSCs in vitro. Notably, two agents, omaveloxolone and niclosamide, showed complete inhibition upon further validation in cyst and microcyst viability assays in vitro after incubation for 3 days, and in an ex vivo cyst viability assay using cysts isolated from the livers of mice infected with E. granulosus, as determined by morphological assessment.ConclusionsThrough the development of a novel HTS assay and by repurposing libraries, we identified omaveloxolone and niclosamide as potent inhibitors against E. granulosus. These compounds show promise as potential anti-echinococcal drugs, and our strategic approach has the potential to promote drug discovery for parasitic infections.Graphical

DTI-LM: language model powered drug-target interaction prediction.

The identification and understanding of drug-target interactions (DTIs) play a pivotal role in the drug discovery and development process. Sequence representations of drugs and proteins in computational model offer advantages such as their widespread availability, easier input quality control, and reduced computational resource requirements. These make them an efficient and accessible tools for various computational biology and drug discovery applications. Many sequence-based DTI prediction methods have been developed over the years. Despite the advancement in methodology, cold start DTI prediction involving unknown drug or protein remains a challenging task, particularly for sequence-based models. Introducing DTI-LM, a novel framework leveraging advanced pretrained language models, we harness their exceptional context-capturing abilities along with neighborhood information to predict DTIs. DTI-LM is specifically designed to rely solely on sequence representations for drugs and proteins, aiming to bridge the gap between warm start and cold start predictions. Large-scale experiments on four datasets show that DTI-LM can achieve state-of-the-art performance on DTI predictions. Notably, it excels in overcoming the common challenges faced by sequence-based models in cold start predictions for proteins, yielding impressive results. The incorporation of neighborhood information through a graph attention network further enhances prediction accuracy. Nevertheless, a disparity persists between cold start predictions for proteins and drugs. A detailed examination of DTI-LM reveals that language models exhibit contrasting capabilities in capturing similarities between drugs and proteins. Source code is available at: https://github.com/compbiolabucf/DTI-LM.

Recent Advances in Nanocatalyzed One-Pot Sustainable Synthesis of Bioactive N, N-Heterocycles with Anticancer Activities: An Outlook of Medicinal Chemistry.

N-heterocycles represent a predominant and unique class of organic chemistry. They have received a lot of attention due to their important chemical, biomedical, and industrial uses. Food and Drug Administration (FDA) approved about 75% of drugs containing N-based heterocycles, which are currently available in the market. N-Heterocyclic compounds exist as the backbone of numerous natural products and act as crucial intermediates for the construction of pharmaceuticals, veterinary items, and agrochemicals frequently. Among N-based heterocyclic compounds, bioactive N,N-heterocycles constitute a broad spectrum of applications in modern drug discovery and development processes. Cefozopran (antibiotic), omeprazole (antiulcer), enviradine (antiviral), liarozole (anticancer), etc., are important drugs containing N,N-heterocycles. The synthesis of N,Nheterocyclic compounds under sustainable conditions is one of the most active fields because of their significant physiological and biological properties as well as synthetic utility. Current research is demanding the development of greener, cheaper, and milder protocols for the synthesis of N,N-heterocyclic compounds to save mother nature by avoiding toxic metal catalysts, extensive application of energy, and the excessive use of hazardous materials. Nanocatalysts play a profound role in sustainable synthesis because of their larger surface area, tiny size, and minimum energy; they are eco-friendly and safe, and they provide higher yields with selectivity in comparison to conventional catalysts. It is increasingly demanding research to design and synthesize novel bioactive compounds that may help to combat cancer since the major causes of death worldwide are due to cancer. Hence, the important uses of nanocatalysts for the one-pot synthesis of biologically potent N,N-heterocycles with anticancer activities have been presented in this review.

Enabling target-aware molecule generation to follow multi objectives with Pareto MCTS

Target-aware drug discovery has greatly accelerated the drug discovery process to design small-molecule ligands with high binding affinity to disease-related protein targets. Conditioned on targeted proteins, previous works utilize various kinds of deep generative models and have shown great potential in generating molecules with strong protein-ligand binding interactions. However, beyond binding affinity, effective drug molecules must manifest other essential properties such as high drug-likeness, which are not explicitly addressed by current target-aware generative methods. In this article, aiming to bridge the gap of multi-objective target-aware molecule generation in the field of deep learning-based drug discovery, we propose ParetoDrug, a Pareto Monte Carlo Tree Search (MCTS) generation algorithm. ParetoDrug searches molecules on the Pareto Front in chemical space using MCTS to enable synchronous optimization of multiple properties. Specifically, ParetoDrug utilizes pretrained atom-by-atom autoregressive generative models for the exploration guidance to desired molecules during MCTS searching. Besides, when selecting the next atom symbol, a scheme named ParetoPUCT is proposed to balance exploration and exploitation. Benchmark experiments and case studies demonstrate that ParetoDrug is highly effective in traversing the large and complex chemical space to discover novel compounds with satisfactory binding affinities and drug-like properties for various multi-objective target-aware drug discovery tasks.

Geometry-Augmented Molecular Representation Learning for Property Prediction.

Accurate molecular representation plays a crucial role in expediting the process of drug discovery. Graph neural networks (GNNs) have demonstrated robust capabilities in molecular representation learning, adept at capturing structural and spatial information in molecular graphs. For molecular representation learning, most previous GNN methods are specialized in dealing with 2D or 3D molecular data formats. By further fusing the geometric attributes and structural features of molecules, we can elevate the performance of molecular representation. To realize this, we present a novel geometry-augmented molecular representation learning model, designed to effectively encode both the 2D structural and 3D spatial information inherent in molecular graphs. By incorporating structural and spatial information as attention biases in the graph Transformer framework, our model offers a comprehensive architecture that introduces molecular structural details at both atom and bond levels. We further propose a geometry information fusion module to encode the geometry information within 3D molecular graphs. The experimental results show the efficacy of our model, demonstrating its ability to achieve competitive performance when compared to state-of-the-art (SOTA) models in various property prediction tasks.

Drug-Target Binding Affinity Prediction in a Continuous Latent Space Using Variational Autoencoders.

Accurate prediction of Drug-Target binding Affinity (DTA) is a daunting yet pivotal task in the sphere of drug discovery. Over the years, a plethora of deep learning-based DTA models have emerged, rendering promising results in predicting the binding affinities between drugs and their target proteins. However, in contrast to the conventional approach of modeling binding affinity in vector spaces, we propose a more nuanced modeling process in a continuous space to account for the diversity of input samples. Initially, the drug is encoded using the Simplified Molecular Input Line Entry System (SMILES), while the target sequences are characterized via a pretrained language model. Subsequently, highly correlative information is extracted utilizing residual gated convolutional neural networks. In a departure from existing deep learning-based models, our model learns the hidden representations of the drugs and targets jointly. Instead of employing two vectors, our hidden representations consist of two Gaussian distributions. To validate the effectiveness of our proposal, we conducted evaluations on commonly utilized benchmark datasets. The experimental outcomes corroborated that our method surpasses the state-of-the-art vectorial representation methods in terms of performance. This approach, therefore, offers potential enhancements in the precision of DTA predictions, potentially contributing to more efficient drug discovery processes.

ParaCPI: A Parallel Graph Convolutional Network for Compound-Protein Interaction Prediction.

Identifying compound-protein interactions (CPIs) is critical in drug discovery, as accurate prediction of CPIs can remarkably reduce the time and cost of new drug development. The rapid growth of existing biological knowledge has opened up possibilities for leveraging known biological knowledge to predict unknown CPIs. However, existing CPI prediction models still fall short of meeting the needs of practical drug discovery applications. A novel parallel graph convolutional network model for CPI prediction (ParaCPI) is proposed in this study. This model constructs feature representation of compounds using a unique approach to predict unknown CPIs from known CPI data more effectively. Experiments are conducted on five public datasets, and the results are compared with current state-of-the-art (SOTA) models under three different experimental settings to evaluate the model's performance. In the three cold-start settings, ParaCPI achieves an average performance gain of 26.75%, 23.84%, and 14.68% in terms of area under the curve compared with the other SOTA models. In addition, the results of the experiments in the case study show ParaCPI's superior ability to predict unknown CPIs based on known data, with higher accuracy and stronger generalization compared with the SOTA models. Researchers can leverage ParaCPI to accelerate the drug discovery process.

Transforming Drug Discovery: The Impact of Artificial Intelligence and Machine Learning from Initial Screening to Clinical Trials

Abstract: The landscape of drug discovery is undergoing a profound transformation driven by the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies. This paper explores the impact of these advancements on various stages of the drug discovery process, from initial screening to clinical trials. Initially, AI and ML are revolutionizing the early stages of drug discovery by enhancing the efficiency of target identification and lead compound screening. Traditional methods, which often rely on labor-intensive processes and high costs, are being supplemented with AI-driven algorithms that analyze vast datasets to identify potential drug targets and predict the biological activity of compounds with unprecedented accuracy. In the drug design and optimization phase, ML models facilitate the prediction of drug interactions and side effects, thus accelerating the development of safer and more effective therapeutics. Advanced simulations and predictive models reduce the reliance on experimental trials, thereby streamlining the development pipeline. The clinical trials phase also benefits significantly from AI and ML. These technologies improve patient stratification by identifying suitable candidates based on genetic and clinical data, optimizing trial designs, and predicting patient responses to treatment. This not only enhances the efficiency of clinical trials but also increases the likelihood of successful outcome. Findings Our research highlights the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on the drug discovery and development process, particularly in enhancing efficiency and precision from initial screening to clinical trials. The integration of AI/ML has shown significant advancements in early-stage drug discovery, where data-driven algorithms enable rapid identification of potential drug candidates, reducing reliance on traditional, labor-intensive methods. In the drug design and optimization phase, AI-driven predictive models have streamlined the process, minimizing the need for extensive physical testing by accurately simulating drug interactions and predicting possible side effects. Additionally, AI and ML are revolutionizing clinical trials by optimizing trial design, improving patient recruitment and retention, and enhancing real-time data monitoring, leading to faster and more reliable trial outcomes. These technologies also support personalized medicine approaches and have proven essential in reducing both the time and cost associated with bringing new therapies to market. Overall, our findings underscore the critical role of AI and ML in reshaping the pharmaceutical landscape, making drug development faster, more cost-effective, and ultimately, more successful in delivering effective treatments to patients.

From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning.

Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that the model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, in a virtual screening on heat shock protein 90 (HSP90) using Dynaformer, 20 candidates are identified and their binding affinities are further experimentally validated. Dynaformer displayed promising results in virtual drug screening, revealing 12 hit compounds (two are in the submicromolar range), including several novel scaffolds. Overall, these results demonstrated that the approach offer a promising avenue for accelerating the early drug discoveryprocess.

Flavopiridol inhibits adipogenesis and improves metabolic homeostasis by ameliorating adipose tissue inflammation in a diet-induced obesity model

Repositioning of FDA approved/clinical phase drugs has recently opened a new opportunity for rapid approval of drugs, as it shortens the overall process of drug discovery and development. In previous studies, we predicted the possibility of better activity profiles of flavopiridol, the FDA approved orphan drug with better fit value 2.79 using a common feature pharmacophore model for anti-adipogenic compounds (CFMPA). The present study aimed to investigate the effect of flavopiridol on adipocyte differentiation and to determine the underlying mechanism. Flavopiridol inhibited adipocyte differentiation in different cell models like 3T3-L1, C3H10T1/2, and hMSCs at 150 nM. Flavopiridol was around 135 times more potent than its parent molecule rohitukine. The effect was mediated through down-regulation of key transcription factors of adipogenesis i.e. Peroxisome proliferator-activated receptor gamma (PPARγ), CCAAT/enhancer-binding protein alpha (C/EBPα), and their downstream targets, including adipocyte protein −2 (aP2) and fatty acid synthase (FAS). Further, results revealed that flavopiridol arrested the cell cycle in G1/S phase during mitotic clonal expansion by suppressing cell cycle regulatory proteins i.e. Cyclins and CDKs. Flavopiridol inhibited insulin-stimulated signalling in the early phase of adipocyte differentiation by downregulation of AKT/mTOR pathway. In addition, flavopiridol improved mitochondrial function in terms of increased oxygen consumption rate (OCR) in mature adipocytes. In the mouse model of diet-induced obesity, flavopiridol attenuated obesity-associated adipose tissue inflammation and improved serum lipid profile, glucose tolerance as well as insulin sensitivity. In conclusion, the FDA approved drug flavopiridol could be placed as a potential drug candidate for the treatment of cancer and obesity comorbid patients.

An improved dataset of force fields, electronic and physicochemical descriptors of metabolic substrates

In silico prediction of xenobiotic metabolism is an important strategy to accelerate the drug discovery process, as candidate compounds often fail in clinical phases due to their poor pharmacokinetic profiles. Here we present MetaQM, a dataset of quantum-mechanical (QM) optimized metabolic substrates, including force field parameters, electronic and physicochemical properties. MetaQM comprises 2054 metabolic substrates extracted from the MetaQSAR database. We provide QM-optimized geometries, General Amber Force Field (FF) parameters for all studied molecules, and an extended set of structural and physicochemical descriptors as calculated by DFT and PM7 methods. The generated data can be used in different types of analysis. FF parameters can be applied to perform classical molecular mechanics calculations as exemplified by the validating molecular dynamics simulations reported here. The calculated descriptors can represent input features for developing improved predictive models for metabolism and drug design, as exemplified in this work. Finally, the QM-optimized molecular structures are valuable starting points for both ligand- and structure-based analyses such as pharmacophore mapping and docking simulations.

MolCFL: A personalized and privacy-preserving drug discovery framework based on generative clustered federated learning

In today’s era of rapid development of large models, the traditional drug development process is undergoing a profound transformation. The vast demand for data and consumption of computational resources are making independent drug discovery increasingly difficult. By integrating federated learning technology into the drug discovery field, we have found a solution that both protects privacy and shares computational power. However, the differences in data held by various pharmaceutical institutions and the diversity in drug design objectives have exacerbated the issue of data heterogeneity, making traditional federated learning consensus models unable to meet the personalized needs of all parties. In this study, we introduce and evaluate an innovative drug discovery framework, MolCFL, which utilizes a multi-layer perceptron (MLP) as the generator and a graph convolutional network (GCN) as the discriminator in a generative adversarial network (GAN). By learning the graph structure of molecules, it generates new molecules in a highly personalized manner and then optimizes the learning process by clustering federated learning, grouping compound data with high similarity. MolCFL not only enhances the model’s ability to protect privacy but also significantly improves the efficiency and personalization of molecular design. MolCFL exhibits superior performance when handling non-independently and identically distributed data compared to traditional models. Experimental results show that the framework demonstrates outstanding performance on two benchmark datasets, with the generated new molecules achieving over 90% in Uniqueness and close to 100% in Novelty. MolCFL not only improves the quality and efficiency of drug molecule design but also, through its highly customized clustered federated learning environment, promotes collaboration and specialization in the drug discovery process while ensuring data privacy. These features make MolCFL a powerful tool suitable for addressing the various challenges faced in the modern drug research and development field.

StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features

Host defense or antimicrobial peptides (AMPs) are promising candidates for protecting host against microbial pathogens for example bacteria, virus, fungi, yeast. Defensins are the type of AMPs that act as potential therapeutic drug agent and perform vital role in various biological process. Conventional Experiments to identify defensin peptides (DPs) are time consuming and expensive. Thus, the shortcomings of wet lab experiments are leveraged by computational methods to accurately predict the functional types of DPs. In this paper, we aim to propose a novel multi-class ensemble-based prediction model called StackDPPred for identifying the properties of DPs. The peptide sequences are encoded using split amino acid composition (SAAC), segmented position specific scoring matrix (SegPSSM), histogram of oriented gradients-based PSSM (HOGPSSM) and feature extraction based graphical and statistical (FEGS) descriptors. Next, principal component analysis (PCA) is used to select the best subset of attributes. After that, the optimized features are fed into single machine learning and stacking-based ensemble classifiers. Furthermore, the ablation study demonstrates the robustness and efficacy of the stacking approach using reduced features for predicting DPs and their families. The proposed StackDPPred method improves the overall accuracy by 13.41% and 7.62% compared to existing DPs predictors iDPF-PseRAAC and iDEF-PseRAAC, respectively on validation test. Additionally, we applied the local interpretable model-agnostic explanations (LIME) algorithm to understand the contribution of selected features to the overall prediction. We believe, StackDPPred could serve as a valuable tool accelerating the screening of large-scale DPs and peptide-based drug discovery process.

Revolutionizing Healthcare: The Emerging Role of Quantum Computing in Enhancing Medical Technology and Treatment.

The healthcare sector faces complex challenges that call for innovative solutions to improve diagnostic accuracy, treatment efficacy, and data management. Quantum computing, with its unique capabilities, holds the potential to revolutionize various aspects of healthcare. This narrative review critically examines the existing literature on the application of quantum computing in healthcare, focusing on its utility in enhancing diagnostics, data processing, and treatment planning. Quantum computing's ability to handle large, complex datasets more efficiently than classical computers can significantly impact domains such as genomics, medical imaging, and personalized medicine. Quantum algorithms can accelerate the identification of genetic markers associated with diseases, facilitate the analysis of medical images, and optimize treatment plans based on individual genetic profiles. Moreover, quantum cryptography offers a robust security solution for safeguarding sensitive patient data, a critical need as healthcare increasingly relies on digital platforms. Despite the promising outlook, the integration of quantum computing into healthcare faces technical, ethical, and regulatory challenges. The delicate nature of quantum hardware, the need for error correction, and the scalability of quantum systems pose barriers to widespread adoption. Additionally, concerns around patient privacy and data security, as well as the need for updated regulatory frameworks, must be addressed. Ongoing research and collaborative efforts involving researchers, healthcare providers, and technology developers are crucial to overcoming these hurdles and realizing the full potential of quantum computing in transforming healthcare. As quantum computing continues to evolve, its impact on the future of healthcare could be profound, leading to earlier disease detection, more personalized treatments, and improved patient outcomes.For instance, quantum computing has already been applied to enhance drug discovery processes, with companies like D-Wave Systems (Burnaby, Canada) demonstrating faster molecular simulations for pharmaceutical research and IBM's (Armonk, USA) quantum systems being used to model chemical reactions for new drug development.

Nuclear Receptors and Stress Response Pathways Associated with the Development of Oral Mucositis Induced by Antineoplastic Agents.

Oral mucositis (OM) is one of the common adverse events associated with cancer treatment that decreases the quality of life and affects treatment outcomes. However, the medications used to manage OM are generally only palliative, and our knowledge of the syndrome is limited. The etiology of the syndrome is thought to be complex and multifactorial. We investigated the trends and characteristics of OM and estimated molecular initiating events (MIEs) associated with the development of the syndrome using the FDA Adverse Event Reporting System. The study of trends and characteristics suggested that OM is significantly more likely to occur in females and nonelderly patients and is likely to be induced by protein kinase inhibitors such as afatinib and everolimus. Next, we used Toxicity Predictor, an in-house quantitative structure-activity relationship system, to estimate OM-associated MIEs. The results revealed that the agonist activity of the human pregnane X receptor, thyroid-stimulating hormone-releasing hormone receptor, and androgen receptor may be associated with OM development. Our study findings are expected to help avoid the risk of OM induction during the drug discovery process and clinical use of antineoplastic agents.

A time‐efficient computational binding affinity estimation protocol with utilization of limited experimental data: A case study for adenosine receptor

AbstractEstimating binding affinity is a crucial step in the drug discovery process. In computer‐aided drug design, this challenge can be divided into two main tasks: finding the correct binding pose and estimating the binding free energy. In this study, we propose a new binding affinity estimation protocol that utilizes molecular docking with limited experimental data and estimates binding affinity using molecular dynamics simulation. A custom scoring function was employed during docking to identify an improved initial binding pose, and the linear interaction energy method with an optimized coefficient was used for binding affinity estimation. The protocol was validated with an external data set and applied to modafinil and its derivatives to rank their binding affinities to adenosine A2A receptors (ADORA2A) as a case study. This approach could be both time‐efficient and valuable for computational drug discovery, particularly when experimental data is limited.

Machine Learning Guided AQFEP: A Fast and Efficient Absolute Free Energy Perturbation Solution for Virtual Screening.

Structure-based methods in drug discovery have become an integral part of the modern drug discovery process. The power of virtual screening lies in its ability to rapidly and cost-effectively explore enormous chemical spaces to select promising ligands for further experimental investigation. Relative free energy perturbation (RFEP) and similar methods are the gold standard for binding affinity prediction in drug discovery hit-to-lead and lead optimization phases, but have high computational cost and the requirement of a structural analog with a known activity. Without a reference molecule requirement, absolute FEP (AFEP) has, in theory, better accuracy for hit ID, but in practice, the slow throughput is not compatible with VS, where fast docking and unreliable scoring functions are still the standard. Here, we present an integrated workflow to virtually screen large and diverse chemical libraries efficiently, combining active learning with a physics-based scoring function based on a fast absolute free energy perturbation method. We validated the performance of the approach in the ranking of structurally related ligands, virtual screening hit rate enrichment, and active learning chemical space exploration; disclosing the largest reported collection of free energy simulations to date.

α,β-Desaturation and Formal β-C(sp3)-H Fluorination of N-Substituted Amines: A Late-Stage Functionalization Strategy Enabled by Electrochemistry.

Incorporation of C(sp3)-F bonds in biologically active compounds is a common strategy employed in medicinal and agricultural chemistry to tune pharmacokinetic and pharmacodynamic properties. Due to the limited number of robust strategies for C(sp3)-H fluorination of complex molecules, time-consuming de novo syntheses of such fluorinated analogs are typically required, representing a major bottleneck in the drug discovery process. In this work, we present a general and operationally simple strategy for site-specific β-C(sp3)-H fluorination of amine derivatives including carbamates, amides, and sulfonamides, which is compatible with a wide range of functional groups including N-heteroarenes. In this approach, an improved electrochemical Shono oxidation is used to set the site of functionalization via net α,β-desaturation to access enamine derivatives. We further developed a series of new transformations of these enamine intermediates to synthesize a variety of β-fluoro-α-functionalized structures, allowing efficient access to pertinent targets to accelerate drug discovery campaigns.

High-Throughput Assessment of Bile Salt Export Pump Inhibition Using RapidFire-MS and DESI-MS.

The bile salt export pump (BSEP) assay is widely used to evaluate the potential for drug-induced liver injury (DILI) early in the drug discovery process. While traditional liquid chromatography-mass spectrometry (LC-MS)-based approaches have been utilized for BSEP activity testing, they have intrinsic limitations in either throughput or the requirement for sample preparation and are difficult to scale up in order to screen drug candidates. Here we demonstrate the use of two different high-throughput MS methods based on solid-phase extraction (SPE) and desorption electrospray ionization (DESI) for high-throughput BSEP activity assessment in a label-free manner, with minimal needs for sample workup, at sampling rates of ∼11 and ∼5.5 s/sample, respectively. Both approaches were validated, compared, and successfully applied to the evaluation of 96 drug candidates for the inhibition of taurocholic acid (TCA) transport using BSEP vesicles.