Covalent hits and where to find them

Covalent hits for drug discovery campaigns are neither fantastic beasts nor mythical creatures, they can be routinely identified through electrophile-first screening campaigns using a suite of different techniques. These include biophysical and biochemical methods, cellular approaches, and DNA-encoded libraries. Employing best practice, however, is critical to success. The purpose of this review is to look at state of the art covalent hit identification, how to identify hits from a covalent library and how to select compounds for medicinal chemistry programmes.

Accelerating BRPF1b hit identification with BioPhysical and Active Learning Screening (BioPALS)

We report the development of BioPhysical and Active Learning Screening (BioPALS); a rapid and versatile hit identification protocol combining AI-powered virtual screening with a GCI-driven biophysical confirmation workflow. Its application to the BRPF1b bromodomain afforded a range of novel micromolar binders with favorable ADMET properties. In addition to the excellent in silico/in vitro confirmation rate demonstrated with BRPF1b, binding kinetics were determined, and binding topologies predicted for all hits. BioPALS is a lean, data-rich, and standardized approach to hit identification applicable to a wide range of biological targets.

Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors

Machine learning-based chemical screening has made substantial progress in recent years. However, these predictions often have low accuracy and high uncertainty when identifying new active chemical scaffolds. Hence, a high proportion of retrieved compounds are not structurally novel. In this study, we proposed a strategy to address this issue by iteratively optimizing an evolutionary chemical binding similarity (ECBS) model using experimental validation data. Various data update and model retraining schemes were tested to efficiently incorporate new experimental data into ECBS models, resulting in a fine-tuned ECBS model with improved accuracy and coverage. To demonstrate the effectiveness of our approach, we identified the novel hit molecules for the mitogen-activated protein kinase kinase 1 (MEK1). These molecules showed sub-micromolar affinity (Kd 0.1-5.3 μM) to MEKs and were distinct from previously-known MEK1 inhibitors. We also determined the binding specificity of different MEK isoforms and proposed potential docking models. Furthermore, using de novo drug design tools, we utilized one of the new MEK inhibitors to generate additional drug-like molecules with improved binding scores. This resulted in the identification of several potential MEK1 inhibitors with better binding affinity scores. Our results demonstrated the potential of this approach for identifying novel hit molecules and optimizing their binding affinities.

Privileged heterocycles for DNA-encoded library design and hit-to-lead optimization

It is well known that heterocyclic compounds play a key role in improving drug activity, target selectivity, physicochemical properties as well as reducing toxicity. In this review, we summarized the representative heterocyclic structures involved in hit compounds which were obtained from DNA-encoded library from 2013 to 2021. In some examples, the state of the art in heterocycle-based DEL synthesis and hit-to-lead optimization are highlighted. We hope that more and more novel heterocycle-based DEL toolboxes and in-depth pharmaceutical research on these lead compounds can be developed to accelerate the discovery of new drugs.

Towards the discovery of potential RdRp inhibitors for the treatment of COVID-19: structure guided virtual screening, computational ADME and molecular dynamics study

Coronavirus disease 2019 (COVID-19) has become a major challenge affecting almost every corner of the world, with more than five million deaths worldwide. Despite several efforts, no drug or vaccine has shown the potential to check the ever-mutating SARS-COV-2. The emergence of novel variants is a major concern increasing the need for the discovery of novel therapeutics for the management of this pandemic. Out of several potential drug targets such as S protein, human ACE2, TMPRSS2 (transmembrane protease serine 2), 3CLpro, RdRp, and PLpro (papain-like protease), RNA-dependent RNA polymerase (RdRP) is a vital enzyme for viral RNA replication in the mammalian host cell and is one of the legitimate targets for the development of therapeutics against this disease. In this study, we have performed structure-based virtual screening to identify potential hit compounds against RdRp using molecular docking of a commercially available small molecule library of structurally diverse and drug-like molecules. Since non-optimal ADME properties create hurdles in the clinical development of drugs, we performed detailed in silico ADMET prediction to facilitate the selection of compounds for further studies. The results from the ADMET study indicated that most of the hit compounds had optimal properties. Moreover, to explore the conformational dynamics of protein-ligand interaction, we have performed an atomistic molecular dynamics simulation which indicated a stable interaction throughout the simulation period. We believe that the current findings may assist in the discovery of drug candidates against SARS-CoV-2.

Deep learning tools for advancing drug discovery and development

A few decades ago, drug discovery and development were limited to a bunch of medicinal chemists working in a lab with enormous amount of testing, validations, and synthetic procedures, all contributing to considerable investments in time and wealth to get one drug out into the clinics. The advancements in computational techniques combined with a boom in multi-omics data led to the development of various bioinformatics/pharmacoinformatics/cheminformatics tools that have helped speed up the drug development process. But with the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL), the conventional drug discovery process has been further rationalized. Extensive biological data in the form of big data present in various databases across the globe acts as the raw materials for the ML/DL-based approaches and helps in accurate identifications of patterns and models which can be used to identify therapeutically active molecules with much fewer investments on time, workforce and wealth. In this review, we have begun by introducing the general concepts in the drug discovery pipeline, followed by an outline of the fields in the drug discovery process where ML/DL can be utilized. We have also introduced ML and DL along with their applications, various learning methods, and training models used to develop the ML/DL-based algorithms. Furthermore, we have summarized various DL-based tools existing in the public domain with their application in the drug discovery paradigm which includes DL tools for identification of drug targets and drug–target interaction such as DeepCPI, DeepDTA, WideDTA, PADME DeepAffinity, and DeepPocket. Additionally, we have discussed various DL-based models used in protein structure prediction, de novo design of new chemical scaffolds, virtual screening of chemical libraries for hit identification, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, metabolite prediction, clinical trial design, and oral bioavailability prediction. In the end, we have tried to shed light on some of the successful ML/DL-based models used in the drug discovery and development pipeline while also discussing the current challenges and prospects of the application of DL tools in drug discovery and development. We believe that this review will be useful for medicinal and computational chemists searching for DL tools for use in their drug discovery projects.

Sequence-based prediction of protein binding regions and drug-target interactions

Identifying drug–target interactions (DTIs) is important for drug discovery. However, searching all drug–target spaces poses a major bottleneck. Therefore, recently many deep learning models have been proposed to address this problem. However, the developers of these deep learning models have neglected interpretability in model construction, which is closely related to a model’s performance. We hypothesized that training a model to predict important regions on a protein sequence would increase DTI prediction performance and provide a more interpretable model. Consequently, we constructed a deep learning model, named Highlights on Target Sequences (HoTS), which predicts binding regions (BRs) between a protein sequence and a drug ligand, as well as DTIs between them. To train the model, we collected complexes of protein–ligand interactions and protein sequences of binding sites and pretrained the model to predict BRs for a given protein sequence–ligand pair via object detection employing transformers. After pretraining the BR prediction, we trained the model to predict DTIs from a compound token designed to assign attention to BRs. We confirmed that training the BRs prediction model indeed improved the DTI prediction performance. The proposed HoTS model showed good performance in BR prediction on independent test datasets even though it does not use 3D structure information in its prediction. Furthermore, the HoTS model achieved the best performance in DTI prediction on test datasets. Additional analysis confirmed the appropriate attention for BRs and the importance of transformers in BR and DTI prediction. The source code is available on GitHub (https://github.com/GIST-CSBL/HoTS).

The expanding reaction toolkit for DNA-encoded libraries

Over the past decade, DNA-encoded libraries (DELs) have emerged as a leading platform for small molecule drug discovery among pharmaceutical companies, biotech companies and academic drug hunters alike. This revolutionary technology has tremendous potential that is yet to be fully realized, as the exploration of therapeutically relevant chemical space is fueled by the ever-expanding repertoire of DNA-compatible reactions used to construct the libraries. Advances in direct coupling reactions, like photo-catalytic cross couplings, unique cyclizations such as the formation of 1,2,4-oxadiazoles, and new functional group transformations are valuable contributions to the DEL reaction toolkit, and indicate where future reaction development efforts should focus in order to maximize the productivity of DELs.

Drug discovery for epigenetics targets

Dysregulation of the epigenome is associated with the onset and progression of several diseases, including cancer, autoimmune, cardiovascular, and neurological disorders. Members from the three families of epigenetic proteins (readers, writers, and erasers) have been shown to be druggable using small-molecule inhibitors. Increasing knowledge of the role of epigenetics in disease and the reversibility of these modifications explain why pharmacological intervention is an attractive strategy for tackling epigenetic-based disease. In this review, we provide an overview of epigenetics drug targets, focus on approaches used for initial hit identification, and describe the subsequent role of structure-guided chemistry optimisation of initial hits to clinical candidates. We also highlight current challenges and future potential for epigenetics-based therapies.

On the design of lead-like DNA-encoded chemical libraries

DNA-encoded libraries (DELs) are becoming an established technology for finding ligands for protein targets. We have abstracted and analysed libraries from the literature to assess the synthesis strategy, selections of reactions and monomers and their propensity to reveal hits. DELs have led to hit compounds across a range of diverse protein classes. The range of reactions and monomers utilised has been relatively limited and the hits are often higher in molecular weight than might be considered ideal. Considerations for future library designs with reference to chemical diversity and lead-like properties are discussed.

Combination of MS Binding Assays and affinity selection mass spectrometry for screening of structurally homogenous libraries as exemplified for a focused oxime library addressing the neuronal GABA transporter 1

This study presents an efficient screening approach based on combination of mass spectrometry (MS) based binding assays (MS Binding Assays) and affinity selection mass spectrometry (ASMS) customized for screening of structurally homogeneous libraries sharing a common mass spectrometric fragmentation pattern. After reaction of a nipecotic acid derivative possessing a hydroxylamine functionality with aldehydes, the resulting oxime library was screened accordingly toward the GABA transporter subtype 1 (GAT1), a drug target for several neurological disorders. After assessing sublibraries' activities for inhibition of reporter ligand binding, hits in active ones were directly identified. This could be achieved by recording mass transitions for the reporter ligand as well as those predicted for the library components in a single LC-MS/MS run with a triple quadrupole mass spectrometer in the multiple reaction monitoring mode. Identification of hits with a predefined affinity could be reliably accomplished by calculation of IC₅₀-values from specific binding concentrations of library constituents and reporter ligand. Application of this strategy revealed six hits, from which two of them were resynthesized for further biological evaluation. Thereby, the best one displayed a pK_i of 7.38 in MS Binding Assays and a pIC₅₀ of 6.82 in [³H]GABA uptake assays for GAT1.

User-Friendly Quantum Mechanics: Applications for Drug Discovery

Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.

Protein-Templated Dynamic Combinatorial Chemistry: Brief Overview and Experimental Protocol

Dynamic combinatorial chemistry (DCC) is a powerful tool to identify bioactive compounds. This efficient technique allows the target to select its own binders and circumvents the need for synthesis and biochemical evaluation of all individual derivatives. An ever-increasing number of publications report the use of DCC on biologically relevant target proteins. This minireview complements previous reviews by focusing on the experimental protocol and giving detailed examples of essential steps and factors that need to be considered, such as protein stability, buffer composition and cosolvents.

Identification and Validation of Novel Autophagy Regulators Using an Endogenous Readout siGENOME Screen

Autophagy is a highly regulated process, and its deregulation can contribute to various diseases, including cancer, immune diseases, and neurodegenerative disorders. Here we describe the design, protocol, and analysis of an imaging-based high-throughput screen with an endogenous autophagy readout. The screen uses a genome-wide siRNA library to identify autophagy regulators in mammalian cells.

The Cell Painting Assay as a Screening Tool for the Discovery of Bioactivities in New Chemical Matter

Multiparametric phenotypic screening based on cellular morphology interrogates many biological pathways simultaneously and is therefore a valuable screening tool for the discovery of new biological activities. The cell painting assay stains various cellular features using six different dyes in one well. By automated image analysis, hundreds of parameters are calculated from the images which deliver a phenotypic profile of the cell. It has been shown that compounds with similar modes of action deliver similar phenotypic profiles. Using a reference set of compounds with known modes of action, it is possible to assign probable modes of action to new compounds and to discover compounds with potentially new modes of action.Here we describe the cell painting assay as a screening tool using a hit identification workflow which has been implemented using open-source software.

Identification by shape-based virtual screening and evaluation of new tyrosinase inhibitors

Targeting tyrosinase is considered to be an effective way to control the production of melanin. Tyrosinase inhibitor is anticipated to provide new therapy to prevent skin pigmentation, melanoma and neurodegenerative diseases. Herein, we report our results in identifying new tyrosinase inhibitors. The shape-based virtual screening was performed to discover new tyrosinase inhibitors. Thirteen potential hits derived from virtual screening were tested by biological determinations. Compound 5186-0429 exhibited the most potent inhibitory activity. It dose-dependently inhibited the activity of tyrosinase, with the IC₅₀ values 6.2 ± 2.0 µM and 10.3 ± 5.4 µM on tyrosine and L-Dopa formation, respectively. The kinetic study of 5186-0429 demonstrated that this compound acted as a competitive inhibitor. We believe the discoveries here could serve as a good starting point for further design of potent tyrosinase inhibitor.

Rational Development of MAGL Inhibitors

Hit identification and hit-to-lead optimization are key steps of the early drug discovery program. Starting from the X-ray crystal structure of the human monoacylglycerol lipase (hMAGL), we herein describe the computational and experimental procedures that we applied for identifying and optimizing a new active inhibitor of this target enzyme. A receptor-based virtual screening method is reported in details, together with enzymatic assays and a first round of hit optimization.

Computational Toxicology and Drug Discovery

The use of computational toxicology methods within drug discovery began in the early 2000s with applications such as predicting bacterial mutagenicity and hERG inhibition. The field has been continuously expanding ever since and the tasks at hand have become more complex. These approaches are now strategically integrated into the risk assessment process, as a complement to in vitro and in vivo methods. Today, computational toxicology can be used in every phase of drug discovery and development, from profiling large libraries early on, to predicting off-target effects in the mid-discovery phase, to assessing potential mutagenic impurities in development and degradants as part of life-cycle management. This chapter provides an overview of the field and describes the application of computational toxicology throughout the entire discovery and development process.

Application of computational methods for anticancer drug discovery, design, and optimization

Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.

A fluorescence polarization based assay for the identification and characterization of polymerase inhibitors

A homogenous fluorescence polarization (FP) assay was developed to monitor the enzymatic activity of polymerases. Under the optimized conditions established in this study, the assay provides highly robust and reproducible data. Miniaturization of the assay for high-throughput screening and compound testing was also performed. The sensitivity of the newly developed assay was confirmed using 2',3'-dideoxyadenosine-5'-triphosphate (ddATP), a chain-elongating inhibitor of the polymerase reaction. Side-by-side comparison of the presented fluorescence polarization assay with already well established PicoGreen® fluorescence intensity assay revealed that the performance of both formats is comparable with good assay sensitivity. However, the direct ratiometric readout of the presented FP assay makes it superior over existing colorimetric and fluorescence intensity based assays in terms of susceptibility to false positives. Moreover, due to its generic nature the presented FP assay can be applied to other polymerases and is compatible with identification of inhibitors and requirements of hit-to-lead programs.

Differential nuclear staining assay for high-throughput screening to identify cytotoxic compounds

As large quantities of novel synthetic molecules continue to be generated there is a challenge to identify therapeutic agents with cytotoxic activity. Here we introduce a Differential Nuclear Staining (DNS) assay adapted to live-cell imaging for high throughput screening (HTS) that utilizes two fluorescent DNA intercalators, Hoechst 33342 and Propidium iodide (PI). Since Hoechst can readily cross cell membranes to stain DNA of living and dead cells, it was used to label the total number of cells. In contrast, PI only enters cells with compromised plasma membranes, thus selectively labeling dead cells. The DNS assay was successfully validated by utilizing well known cytotoxic agents with fast or slow cytotoxic activities. The assay was found to be suitable for HTS with Z' factors ranging from 0.86 to 0.60 for 96 and 384-well formats, respectively. Furthermore, besides plate-to-plate reproducibility, assay quality performance was evaluated by determining ratios of signal-to-noise and signal-to-background, as well as coefficient of variation, which resulted in adequate values and validated the assay for HTS initiatives. As proof of concept, eighty structurally diverse compounds from a small molecule library were screened in a 96-well plate format using the DNS assay. Using this DNS assay, six hits with cytotoxic properties were identified and all of them were also successfully identified by using the commercially available MTS assay (CellTiter 96® Cell Proliferation Assay). In addition, the DNS and a flow cytometry assay were used to validate the activity of the cytotoxic compounds. The DNS assay was also used to generate dose-response curves and to obtain CC50 values. The results indicate that the DNS assay is reliable and robust and suitable for primary and secondary screens of compounds with potential cytotoxic activity.