Hit Discovery Research Articles

Antibody-Secreting Cell Isolation from Different Species for Microfluidic Antibody Hit Discovery.

The recent advent of microfluidic-assisted antibody hit discovery as standard methodology accelerated pharmaceutical research. While work on compatible recombinant antibody library approaches is ongoing, the major source of antibody-secreting cells (ASCs) remains to be primary B cells of mostly rodent origin. As fainting viability and secretion rates can lead to false-negative screening results, careful preparation of these cells is an essential prerequisite for successful hit discovery. We here describe procedures to enrich plasma cells from relevant tissues of mice and rats and plasmablasts from human blood donations. Although freshly prepared ASCs yield the most robust results, suitable freezing and thawing protocols to preserve the viability and antibody secretory function can circumvent extensive process time and allow transferring of samples between laboratories. An optimized procedure is described to yield similar secretion rates after prolonged storage when compared to freshly prepared cells. Finally, the identification of ASC-containing samples can increase the probability of success of droplet-based microfluidics-two methods for pre- or in-droplet staining are described. In summary, the preparative methods described herein can facilitate robust and successful microfluidic antibody hit discovery.

Discovery of hit compounds for methyl-lysine reader proteins from a target class DNA-encoded library.

Methyl-lysine (Kme) reader domains are prevalent in chromatin regulatory proteins which bind post-translational modification sites to recruit repressive and activating factors; therefore, these proteins play crucial roles in cellular signaling and epigenetic regulation. Proteins that contain Kme domains are implicated in various diseases, including cancer, making them attractive therapeutic targets for drug and chemical probe discovery. Herein, we report on expanding the utility of a previously reported, Kme-focused DNA-encoded library (DEL), UNCDEL003, as a screening tool for hit discovery through the specific targeting of Kme reader proteins. As an efficient method for library generation, focused DELs are designed based on structural and functional features of a specific class of proteins with the intent of novel hit discovery. To broadly assess the applicability of our library, UNCDEL003 was screened against five diverse Kme reader protein domains (53BP1 TTD, KDM7B JmjC-PHD, CDYL2 CD, CBX2 CD, and LEDGF PWWP) with varying structures and functions. From these screening efforts, we identified hit compounds which contain unique chemical scaffolds distinct from previously reported ligands. The selected hit compounds were synthesized off-DNA and confirmed using primary and secondary assays and assessed for binding selectivity. Hit compounds from these efforts can serve as starting points for additional development and optimization into chemical probes to aid in further understanding the functionality of these therapeutically relevant proteins.

CSAlign and CSAlign-Dock: Structure alignment of ligands considering full flexibility and application to protein–ligand docking

Structure prediction of protein–ligand complexes, called protein–ligand docking, is a critical computational technique that can be used to understand the underlying principle behind the protein functions at the atomic level and to design new molecules regulating the functions. Protein-ligand docking methods have been employed in structure-based drug discovery for hit discovery and lead optimization. One of the important technical challenges in protein–ligand docking is to account for protein conformational changes induced by ligand binding. A small change such as a single side-chain rotation upon ligand binding can hinder accurate docking. Here we report an increase in docking performance achieved by structure alignment to known complex structures. First, a fully flexible compound-to-compound alignment method CSAlign is developed by global optimization of a shape score. Next, the alignment method is combined with a docking algorithm to dock a new ligand to a target protein when a reference protein–ligand complex structure is available. This alignment-based docking method, called CSAlign-Dock, showed superior performance to ab initio docking methods in cross-docking benchmark tests. Both CSAlign and CSAlign-Dock are freely available as a web server at https://galaxy.seoklab.org/csalign.

Construction of a synthetic methodology-based library and its application in identifying a GIT/PIX protein–protein interaction inhibitor

In recent years, the flourishing of synthetic methodology studies has provided concise access to numerous molecules with new chemical space. These compounds form a large library with unique scaffolds, but their application in hit discovery is not systematically evaluated. In this work, we establish a synthetic methodology-based compound library (SMBL), integrated with compounds obtained from our synthetic researches, as well as their virtual derivatives in significantly larger scale. We screen the library and identify small-molecule inhibitors to interrupt the protein–protein interaction (PPI) of GIT1/β-Pix complex, an unrevealed target involved in gastric cancer metastasis. The inhibitor 14-5-18 with a spiro[bicyclo[2.2.1]heptane-2,3’-indolin]−2’-one scaffold, considerably retards gastric cancer metastasis in vitro and in vivo. Since the PPI targets are considered undruggable as they are hard to target, the successful application illustrates the structural specificity of SMBL, demonstrating its potential to be utilized as compound source for more challenging targets.

Few-Shot Learning for Low-Data Drug Discovery.

The discovery of new hits through ligand-based virtual screening in drug discovery is essentially a low-data problem, as data acquisition is both difficult and expensive. The requirement for large amounts of training data hinders the application of conventional machine learning techniques to this problem domain. This work explores few-shot machine learning for hit discovery and lead optimization. We build on the state-of-the-art and introduce two new metric-based meta-learning techniques, Prototypical and Relation Networks, to this problem domain. We also explore using different embeddings, namely, extended-connectivity fingerprints (ECFP) and embeddings generated through graph convolutional networks (GCN), as inputs to neural networks for classification. This study shows that learned embeddings through GCNs consistently perform better than extended-connectivity fingerprints for toxicity and LBVS experiments. We conclude that the effectiveness of few-shot learning is highly dependent on the nature of the data. Few-shot learning models struggle to perform consistently on MUV and DUD-E data, in which the active compounds are structurally distinct. However, on Tox21 data, the few-shot models perform well, and we find that Prototypical Networks outperform the state-of-the-art, which is based on the Matching Networks architecture. Additionally, training these networks is substantially faster (up to 190%) and therefore takes a fraction of the time to train for comparable, or better, results.

On drug discovery against infectious diseases and academic medicinal chemistry contributions.

This perspective is an attempt to document the problems that medicinal chemists are facing in drug discovery. It is also trying to identify relevant/possible, research areas in which academics can have an impact and should thus be the subject of grant calls. Accordingly, it describes how hit discovery happens, how compounds to be screened are selected from available chemicals and the possible reasons for the recurrent paucity of useful/exploitable results reported. This is followed by the successful hit to lead stories leading to recent and original antibacterials which are, or about to be, used in human medicine. Then, illustrated considerations and suggestions are made on the possible inputs of academic medicinal chemists. This starts with the observation that discovering a “good” hit in the course of a screening campaign still rely on a lot of luck – which is within the reach of academics –, that the hit to lead process requires a lot of chemistry and that if public–private partnerships can be important throughout these stages, they are absolute requirements for clinical trials. Concerning suggestions to improve the current hit success rate, one academic input in organic chemistry would be to identify new and pertinent chemical space, design synthetic accesses to reach these and prepare the corresponding chemical libraries. Concerning hit to lead programs on a given target, if no new hits are available, previously reported leads along with new structural data can be pertinent starting points to design, prepare and assay original analogues. In conclusion, this text is an actual plea illustrating that, in many countries, academic research in medicinal chemistry should be more funded, especially in the therapeutic area neglected by the industry. At the least, such funds would provide the intensive to secure series of hopefully relevant chemical entities which appears to often lack when considering the results of academic as well as industrial screening campaigns.

Metal‐Free and Open‐Air Arylation Reactions of Diaryliodonium Salts for DNA‐Encoded Library Synthesis (Adv. Sci. 26/2022)

Meeting Diaryliodonium salts Chemistry with DNA-Encoded Library DNA-encoded library (DEL) is a powerful technique for hit discovery in medicinal chemistry. In article number 2202790, Hongtao Xu, Peixiang Ma, Wei Hou, Guang Yang, and co-workers develope the first on-DNA meeting diaryliodonium salts (DAI) chemistry and realize the selective arylation of phenols and oximes with both high yield and DNA fidelity via substrate activation strategy.

Triazine-Based Covalent DNA-Encoded Libraries for Discovery of Covalent Inhibitors of Target Proteins.

Since ibrutinib was approved by the FDA as an effective monotherapy for chronic lymphocytic leukemia (CLL) and multilymphoma, more and more FDA-approved covalent drugs are coming back into the market. On this occasion, the resurgence of interest in covalent drugs calls for more hit discovery techniques. However, the limited numbers of covalent libraries prevent the development of this area. Herein, we report the design of covalent DNA-encoded library (DEL) and its selection method for the discovery of covalent inhibitors for target proteins. These triazine-based covalent DELs yielded potent compounds after covalent selection against target proteins, including Bruton's Tyrosine Kinase (BTK), Janus kinase 3 (JAK3), and peptidyl-prolyl cis/trans isomerase NIMA-interacting-1 (Pin1).

CHARMM‐GUIhigh‐throughput simulator for efficient evaluation of protein–ligand interactions with different force fields

AbstractMolecular docking is one of the most popular computational tools for the hit discovery step in drug design. However, there is ample room for improvement of docking's ability to identify correct binding modes and discriminate active from decoy compounds. Molecular dynamics (MD) simulations of protein–ligand docking structures have been shown to be effective in improving docking results. Here, we present CHARMM‐GUI high‐throughput simulator (HTS) that prepares MD simulation systems and inputs for multiple protein–ligand complex structures in a high‐throughput manner. HTS supports commonly used MD programs (NAMD, GROMACS, AMBER, OpenMM, GENESIS, Desmond, LAMMPS, and Tinker) along with various force field combinations for protein and ligand, including CHARMM36m, Amber (ff19SB/ff14SB), OPLS‐AA/M, CGenFF, GAFF2, and OpenFF. Validation tests using Miller and the directory of useful decoys‐enhanced (DUD‐E) datasets demonstrate that short MD simulations using HTS‐generated systems and simple ligand RMSD calculations consistently outperform docking results. Specifically, MD simulations can better identify correct ligand‐binding modes among top 10 binding poses as compared to docking scores. In addition, MD simulations can better discriminate active from decoy compounds in the DUD‐E dataset than docking scores for both soluble and membrane proteins. We expect that HTS can be a useful tool to facilitate the hit discovery process in drug design by improving docking results.

Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation

Recently, graph neural networks (GNNs) have revolutionized the field of chemical property prediction and achieved state-of-the-art results on benchmark data sets. Compared with the traditional descriptor- and fingerprint-based QSAR models, GNNs can learn task related representations, which completely gets rid of the rules defined by experts. However, due to the lack of useful prior knowledge, the prediction performance and interpretability of the GNNs may be affected. In this study, we introduced a new GNN model called RG-MPNN for chemical property prediction that integrated pharmacophore information hierarchically into message-passing neural network (MPNN) architecture, specifically, in the way of pharmacophore-based reduced-graph (RG) pooling. RG-MPNN absorbed not only the information of atoms and bonds from the atom-level message-passing phase, but also the information of pharmacophores from the RG-level message-passing phase. Our experimental results on eleven benchmark and ten kinase data sets showed that our model consistently matched or outperformed other existing GNN models. Furthermore, we demonstrated that applying pharmacophore-based RG pooling to MPNN architecture can generally help GNN models improve the predictive power. The cluster analysis of RG-MPNN representations and the importance analysis of pharmacophore nodes will help chemists gain insights for hit discovery and lead optimization.Graphical

Discovery of novel orexin receptor antagonists using a 1,3,5-trioxazatriquinane bearing multiple effective residues (TriMER) library

Structurally diverse small compounds are utilized to obtain hit compounds that have suitable pharmacophores in appropriate three-dimensional conformations for the target drug receptors. We have focused on the 1,3,5-trioxazatriquinane skeleton, which has a rigid bowl-like structure enabling the diverse orientation of side chain units, leading to a novel small-scale focused library based on the skeleton. In the library screening for the orexin receptor, some of the compounds showed orexin receptor antagonistic activity with a high hit rate of 7%. By optimizing the hit compounds, we discovered a potent dual orexin receptor antagonist, 38b, and a selective orexin 1 receptor antagonist, 41b carrying the same plane structure. Both compounds showed reasonable brain permeability and beneficial effects when administered intraperitoneally to wild-type mice. Docking simulations of their eutomers, (−)-38b and (+)-41b, with orexin receptors suggested that the interaction between the 1,3,5-trioxazatriquinane core structure and the hydrophobic subpocket in orexin receptors enables a U-shape structure, which causes tight van der Waals interactions with the receptors similar to SB-334867, a selective orexin 1 receptor antagonist. These results indicate that the library approach utilizing the 1,3,5-trioxazatriquinanes bearing multiple effective residues (TriMERs) might be useful for the hit discovery process targeting not only opioid and orexin receptors but other G-protein coupled receptors.

Streamlining the Transition From Yeast Surface Display of Antibody Fragment Immune Libraries to the Production as IgG Format in Mammalian Cells.

Yeast-surface display (YSD) is commonly applied to screen Fab immune or naïve libraries for binders of predefined target molecules. However, reformatting of isolated variants represents a time-intensive bottleneck. Herein, we present a novel approach to facilitate a lean transition from antibody screening using YSD Fab libraries to the production of full-length IgG antibodies in Expi293-F cells. In this study, utilizing Golden Gate Cloning (GGC) and a bidirectional promoter system, an exemplary Fab-displaying YSD library was generated based on immunised transgene rats. After subsequent screening for antigen-specific antibody candidates by fluorescence-activated cell sorting (FACS), the Fab-encoding genes were subcloned into a bidirectional mammalian expression vector, exhibiting CH2-CH3 encoding genes, in a GGC-mediated, PCR-free manner. This novel, straightforward and time-saving workflow allows the VH/VL pairing to be preserved. This study resulted in antibody variants exhibiting suitable biophysical properties and covered a broad VH diversity after two rounds of FACS screening, as revealed by NGS analysis. Ultimately, we demonstrate that the implication of such a gene transfer system streamlines antibody hit discovery efforts, allowing the faster characterisation of antibodies against a plethora of targets that may lead to new therapeutic agents.

Visible Light-Promoted Divergent Benzoheterocyclization from Aldehydes for DNA-Encoded Chemical Libraries.

Benzoheterocyclics have been widely adopted as drug-like core scaffolds that can be incorporated into DNA-encoded chemical library technology for high-throughput hit discovery. Here, we present a visible light-promoted divergent synthesis of on-DNA benzoheterocycles from aldehydes. Four types of DNA-conjugated benzoheterocyclics were obtained under mild conditions with a broad substrate scope. A cross substrate scope study, together with enzymatic ligation and subsequent chemical diversifications, were conducted, demonstrating the feasibility of this approach in DNA-encoded chemical library construction.

D3AI-CoV: a deep learning platform for predicting drug targets and for virtual screening against COVID-19.

Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug discovery and development. Therefore, target prediction and virtual screening are of great importance for developing highly effective drugs against COVID-19. Here we present D3AI-CoV, a platform for target prediction and virtual screening for the discovery of anti-COVID-19 drugs. The platform is composed of three newly developed deep learning-based models i.e., MultiDTI, MPNNs-CNN and MPNNs-CNN-R models. To compare the predictive performance of D3AI-CoV with other methods, an external test set, named Test-78, was prepared, which consists of 39 newly published independent active compounds and 39 inactive compounds from DrugBank. For target prediction, the areas under the receiver operating characteristic curves (AUCs) of MultiDTI and MPNNs-CNN models are 0.93 and 0.91, respectively, whereas the AUCs of the other reported approaches range from 0.51 to 0.74. For virtual screening, the hit rate of D3AI-CoV is also better than other methods. D3AI-CoV is available for free as a web application at http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoV/index.php, which can serve as a rapid online tool for predicting potential targets for active compounds and for identifying active molecules against a specific target protein for COVID-19 treatment.

Switchable DNA-Encoded Chemical Library: Interconversion between Double- and Single-Stranded DNA Formats.

DNA-encoded chemical libraries (DEL) have attracted substantial attention due to the infinite possibility for hit discovery in both pharmaceutical companies and academia. The encoding method is the initial step of DEL construction and one of the cornerstones of DEL applications. Classified by the DNA format, the existing DEL encoding strategies were categorized into single-stranded DNA-based strategies and double-stranded DNA-based strategies. The two DEL formats have their unique advantages but are usually incompatible with each other. To address this issue, we propose the concept of interconversion between double- and single-stranded DEL based on the "reversible covalent headpiece (RCHP)" design, which combines maximum robustness of synthesis with extraordinary flexibility of applications in distinct setups. Future opportunities in this field are also proposed to advance DEL technology to a comprehensive drug discovery platform.

It all clicks together: In silico drug discovery becoming mainstream

It all clicks together: In silico drug discovery becoming mainstream

Virtual screening of molecular databases for potential inhibitors of the NSP16/NSP10 methyltransferase from SARS-CoV-2

COVID-19 is a disease caused by the SARS-CoV-2 virus and represents one of the greatest health problems that humanity faces at the moment. Therefore, efforts have been made with the objective of seeking therapies that could be effective in combating this problematic. In the search for ligands, computational chemistry plays an essential role, since it allows the screening of thousands of molecules on a given target, in order to save time and money for the in vitro or in vivo pharmacological stage. In this paper, we perform a virtual screening by docking looking for potential inhibitors of the NSP16-NSP10 protein dimer (methyltransferase) from SARS-CoV-2, by evaluating a homemade databank of molecules found in plants of the Caatinga Brazilian biome, compounds from ZINC online molecular database, as well as structural analogues of the enzymatic cofactor s-adenosylmethionine (SAM) and a known inhibitor in the literature, sinefungin (SFG), provided at PubChem database. All the evaluated sets presented molecules that deserve attention, highlighting four compounds from ZINC as the most promising ligands. These results contribute to the discovery of new molecular hits, in the search of potential agents against SARS-CoV-2 virus, still unveiling a pathway that can be used in combined therapies.

Basics, types and applications of molecular docking: A review

From hit discovery through lead optimization and beyond, computational methods have become an essential part of many drugs development processes. There are typically several steps in the docking process, and each one provides a new level of complexity. Docking methods are used to place small molecules in the active region of the enzyme. In addition to these methods, scoring functions are used to estimate a compound's biological activity by looking at how it interacts with prospective targets. Molecular docking is considered to be the most widely utilized computational phenomenon in the field of computer-aided drug design (CADD). It is being utilized at the academic level as well as in pharmaceutical companies for the lead discovery process. Molecular docking is mainly associated with two terms: ligand and protein. Protein is the target site where ligand may bind to give specific activity. Molecular docking provides information on the ability of the ligand to bind with protein which is known as binding affinity. Applications of molecular docking in drug development have evolved significantly since it was first created to aid in the study of molecular recognition processes between small and large compounds. This review emphasizes the basic features of molecular docking along with the types, approaches and applications.

Metabolomics-assisted discovery of a new anticancer GLS-1 inhibitor chemotype from a nortopsentin-inspired library: From phenotype screening to target identification

The enzyme glutaminase-1 (GLS-1) has shown a clear and coherent implication in the progression and exacerbation of different aggressive tumors such as glioblastoma, hepatocarcinoma, pancreas, bone, and triple-negative breast cancer. Few chemotypes are currently available as selective GLS-1 inhibitors, and still, fewer of them are at the clinical stage. In the present paper, starting from a naturally-inspired antitumor compound library, metabolomics has been used to putatively identify the molecular mechanism underlying biological activity. GLS-1 was identified as a potential target. Biochemical analysis confirmed the hypothesis leading to the identification of a new hit compound acting as a GLS-1 selective inhibitor (IC50 = 3.96 ± 1.05 μM), compared to the GLS-2 isoform (IC50 = 12.90 ± 0.87 μM), with remarkable antitumor potency over different aggressive tumor cell lines. Molecular modelling studies revealed new insight into the drug-target interaction providing robust SAR clues for the rational hit-to-lead development. The approach undertaken underlines the wide potential of metabolomics applied to drug discovery, particularly in target identification and hit discovery following phenotype screening.

Novel Big Data-Driven Machine Learning Models for Drug Discovery Application.

Most contemporary drug discovery projects start with a ‘hit discovery’ phase where small chemicals are identified that have the capacity to interact, in a chemical sense, with a protein target involved in a given disease. To assist and accelerate this initial drug discovery process, ’virtual docking calculations’ are routinely performed, where computational models of proteins and computational models of small chemicals are evaluated for their capacities to bind together. In cutting-edge, contemporary implementations of this process, several conformations of protein targets are independently assayed in parallel ‘ensemble docking’ calculations. Some of these protein conformations, a minority of them, will be capable of binding many chemicals, while other protein conformations, the majority of them, will not be able to do so. This fact that only some of the conformations accessible to a protein will be ’selected’ by chemicals is known as ’conformational selection’ process in biology. This work describes a machine learning approach to characterize and identify the properties of protein conformations that will be selected (i.e., bind to) chemicals, and classified as potential binding drug candidates, unlike the remaining non-binding drug candidate protein conformations. This work also addresses the class imbalance problem through advanced machine learning techniques that maximize the prediction rate of potential protein molecular conformations for the test case proteins ADORA2A (Adenosine A2a Receptor) and OPRK1 (Opioid Receptor Kappa 1), and subsequently reduces the failure rates and hastens the drug discovery process.