Computational Fragment-Based Drug Design: Current Trends, Strategies, and Applications

Fine-Tuning a Genetic Algorithm for CAMD: A Screening-Guided Warm Start

More sustainable chemical processes require the selection of suitable molecules, which can be supported by computer-aided molecular design (CAMD). CAMD often generates and evaluates molecular structures using genetic algorithms. However, genetic algorithms can suffer from slow convergence, and might yield suboptimal solutions. In response to these challenges, this work presents a method to fine-tune a genetic algorithm for CAMD. The proposed method builds on the COSMO-CAMD framework that utilizes a genetic algorithm for solving optimization-based molecular design problems and COSMO-RS for predicting physical properties of molecules. The key idea of the proposed method is to integrate results from a fast large-scale molecular screening into the molecular design framework through an automated fragmentation procedure. By generating a promising initial population and constructing a tailored fragment library, our method enables a targeted initialization of the genetic algorithm, referred to as warm-start. The proposed method is applied in two case studies to design solvents for extracting γ-valerolactone and phenol, respectively, from aqueous solutions. Compared to the benchmark method, the warm-started COSMO-CAMD framework achieves a 70% faster convergence, discovers 4-fold more top-performing candidate molecules, and identifies seven tailored molecular fragments, culminating in the discovery of two novel solvents specifically for the phenol case. The optimal solvent is found in all computational runs. Overall, the warm-started COSMO-CAMD framework significantly improves efficiency, effectiveness, and robustness of molecular design.

The role and application of bioinformatics techniques and tools in drug discovery

The process of drug discovery and development is both lengthy and intricate, demanding a substantial investment of time and financial resources. Bioinformatics techniques and tools can not only accelerate the identification of drug targets and the screening and refinement of drug candidates, but also facilitate the characterization of side effects and the prediction of drug resistance. High-throughput data from genomics, transcriptomics, proteomics, and metabolomics make significant contributions to mechanics-based drug discovery and drug reuse. This paper summarizes bioinformatics technologies and tools in drug research and development and their roles and applications in drug research and development, aiming to provide references for the development of new drugs and the realization of precision medicine.

Fragment-to-Lead Medicinal Chemistry Publications in 2023

This Perspective summarizes successful fragment-to-lead (F2L) studies that were published in 2023 and is the ninth installment in an annual series. A tabulated summary of the relevant articles published in 2023 is provided (17 entries from 16 articles), and a comparison of the target classes, screening methods, and overall fragment or lead property trends for 2023 examples and for the combined entries over the years 2015-2023 is discussed. In addition, we identify several trends and innovations in the 2023 literature that promise to further increase the success of fragment-based drug discovery (FBDD), particularly in the areas of NMR and virtual screening, fragment library design, and fragment linking.

DigFrag as a digital fragmentation method used for artificial intelligence-based drug design

Fragment-Based Drug Design (FBDD) plays a pivotal role in the field of drug discovery and development. The construction of high-quality fragment libraries is a critical step in FBDD. Conventional fragmentation approaches often rely on rigid rules and chemical intuition, limiting their adaptability to diverse molecular structures. The rapid development of Artificial Intelligence (AI) technology offers a transformative opportunity to rethink traditional methods. Here, we present DigFrag, a digital fragmentation method that highlights important substructures by focusing locally within the molecular graph. In addition, we feed the fragments segmented by machine intelligence and human expertise into the deep generative model to compare the preference for data from different sources. Experimental results show that the structural diversity of fragments segmented by DigFrag is higher, and more desirable compounds are generated based on these fragments. These results also demonstrate that data generated based on AI methods may be more suitable for AI models. Moreover, a user-friendly platform called MolFrag ( https://dpai.ccnu.edu.cn/MolFrag/ ) is developed based on various fragmentation techniques to support molecular segmentation.

In Silico Investigation against Inhibitors of Alpha-Amylase Using Structure-based Screening, Molecular Docking, and Molecular Simulations Studies

Type-II diabetes mellitus is a chronic disorder that results from fluctuations in the glucose level leading to hyperglycemia with severe adverse effects increasing worldwide. Alpha-Amylase is the key enzyme involved in the mechanism of glucose formation therefore Alpha-Amylase inhibitors have become a therapeutic target in the development of new leads as they have the potential to suppress glucose levels. Existing drugs targeting Alpha-Amylase highlight major drawbacks in terms of poor absorption rate that causes several gastrointestinal issues. So, this research is aimed to develop novel inhibitors interacting with Alpha-Amylase's active site using structural-based screening, binding pattern analysis, and molecular dynamic simulation. Hence, to search for a potential lead, we analyzed a total of 133 valiolamine derivatives and 535 desoxynojirimycin derivatives that exhibited drug-like properties screened through Lipinski filters. Virtual screening followed by binding interaction analysis we identified ten compounds that exhibited better binding energy scores compared to the standard drugs voglibose and miglitol, used in our study. The docking analysis, ADMET and metabolic site prediction estimated the best top two compounds with good drug profiles. Further, top compounds VG9 and VG15 were promoted to simulation study using the Biovia Discovery study to access the stability at a time interval of 100 ns. MD simulation results revealed that our compound VG9 possesses better conformational stability in the complex to the active site residues of Alpha-Amylase target protein than standard drug voglibose. Thus, our investigation revealed that compound VG9 also exhibits the best pharmacokinetic as well as binding affinity results and could act as a potential lead compound targeting Alpha-Amylase for Type II diabetes.

Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening

Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While mathematical modeling and machine learning have become vital in predicting drug-target interactions and properties, there is untapped potential in computational drug discovery due to the vast and complex chemical space. This paper builds on our recently published computational fragment-based drug discovery (FBDD) method called fragment databases from screened ligand drug discovery (FDSL-DD). FDSL-DD uses in silico screening to identify ligands from a vast library, fragmenting them while attaching specific attributes based on predicted binding affinity and interaction with the target subdomain. In this paper, we further propose a two-stage optimization method that utilizes the information from prescreening to optimize computational ligand synthesis. We hypothesize that using prescreening information for optimization shrinks the search space and focuses on promising regions, thereby improving the optimization for candidate ligands. The first optimization stage assembles these fragments into larger compounds using genetic algorithms, followed by a second stage of iterative refinement to produce compounds with enhanced bioactivity. To demonstrate broad applicability, the methodology is demonstrated on three diverse protein targets found in human solid cancers, bacterial antimicrobial resistance, and the SARS-CoV-2 virus. Combined, the proposed FDSL-DD and a two-stage optimization approach yield high-affinity ligand candidates more efficiently than other state-of-the-art computational FBDD methods. We further show that a multiobjective optimization method accounting for drug-likeness can still produce potential candidate ligands with a high binding affinity. Overall, the results demonstrate that integrating detailed chemical information with a constrained search framework can markedly optimize the initial drug discovery process, offering a more precise and efficient route to developing new therapeutics.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning

Fragment-based drug discovery (FBDD) is widely used in drug design. One useful strategy in FBDD is designing linkers for linking fragments to optimize their molecular properties. In the current study, we present a novel generative fragment linking model, GRELinker, which utilizes a gated-graph neural network combined with reinforcement and curriculum learning to generate molecules with desirable attributes. The model has been shown to be efficient in multiple tasks, including controlling log P, optimizing synthesizability or predicted bioactivity of compounds, and generating molecules with high 3D similarity but low 2D similarity to the lead compound. Specifically, our model outperforms the previously reported reinforcement learning (RL) built-in method DRlinker on these benchmark tasks. Moreover, GRELinker has been successfully used in an actual FBDD case to generate optimized molecules with enhanced affinities by employing the docking score as the scoring function in RL. Besides, the implementation of curriculum learning in our framework enables the generation of structurally complex linkers more efficiently. These results demonstrate the benefits and feasibility of GRELinker in linker design for molecular optimization and drug discovery.

Recent Deep Learning Applications to Structure-Based Drug Design

Identification and optimization of small molecules that bind to and modulate protein function is a crucial step in the early stages of drug development. For decades, this process has benefitted greatly from the use of computational models that can provide insights into molecular binding affinity and optimization. Over the past several years, various types of deep learning models have shown great potential in improving and enhancing the performance of traditional computational methods. In this chapter, we provide an overview of recent deep learning-based developments with applications in drug discovery. We classify these methods into four subcategories dependent on the task each method is aiming to solve. For each subcategory, we provide the general framework of the approach and discuss individual methods.

Collective and Coordinated Conformational Changes Determine Agonism or Antagonism at the Human Trace Amine-Associated Receptor 1

The human trace amine-associated receptor (hTAAR1), a G protein-coupled receptor, has been postulated as a new target in the treatment of neuropsychiatric conditions. The mechanism associated with activation or inactivation by agonists or antagonists in hTAAR1 and other GPCRs has not yet been fully elucidated. In this study, we combined computational methods including homology modeling, docking, and molecular dynamic simulations to reveal novel conformational changes associated with agonist and antagonist interactions in hTAAR1. Our findings suggest a differential cascade of coordinated movements based on the presence of either an agonist or antagonist and primarily involving the second extracellular loop, transmembrane domain 5, and the third intracellular domains of hTAAR1. Our study provides an opportunity to predict the effects on new ligands with agonistic or antagonistic activity at hTAAR1 based on the reported conformational changes.

Alchemical Free Energy Calculations on Membrane-Associated Proteins

Membrane proteins have diverse functions within cells and are well-established drug targets. The advances in membrane protein structural biology have revealed drug and lipid binding sites on membrane proteins, while computational methods such as molecular simulations can resolve the thermodynamic basis of these interactions. Particularly, alchemical free energy calculations have shown promise in the calculation of reliable and reproducible binding free energies of protein-ligand and protein-lipid complexes in membrane-associated systems. In this review, we present an overview of representative alchemical free energy studies on G-protein-coupled receptors, ion channels, transporters as well as protein-lipid interactions, with emphasis on best practices and critical aspects of running these simulations. Additionally, we analyze challenges and successes when running alchemical free energy calculations on membrane-associated proteins. Finally, we highlight the value of alchemical free energy calculations calculations in drug discovery and their applicability in the pharmaceutical industry.

SILVR: Guided Diffusion for Molecule Generation

Computationally generating new synthetically accessible compounds with high affinity and low toxicity is a great challenge in drug design. Machine learning models beyond conventional pharmacophoric methods have shown promise in the generation of novel small-molecule compounds but require significant tuning for a specific protein target. Here, we introduce a method called selective iterative latent variable refinement (SILVR) for conditioning an existing diffusion-based equivariant generative model without retraining. The model allows the generation of new molecules that fit into a binding site of a protein based on fragment hits. We use the SARS-CoV-2 main protease fragments from Diamond XChem that form part of the COVID Moonshot project as a reference dataset for conditioning the molecule generation. The SILVR rate controls the extent of conditioning, and we show that moderate SILVR rates make it possible to generate new molecules of similar shape to the original fragments, meaning that the new molecules fit the binding site without knowledge of the protein. We can also merge up to 3 fragments into a new molecule without affecting the quality of molecules generated by the underlying generative model. Our method is generalizable to any protein target with known fragments and any diffusion-based model for molecule generation.

Construction of Histone-Protein Complex Structures by Peptide Growing

The structures of histone complexes are master keys to epigenetics. Linear histone peptide tails often bind to shallow pockets of reader proteins via weak interactions, rendering their structure determination challenging. In the present study, a new protocol, PepGrow, is introduced. PepGrow uses docked histone fragments as seeds and grows the full peptide tails in the reader-binding pocket, producing atomic-resolution structures of histone-reader complexes. PepGrow is able to handle the flexibility of histone peptides, and it is demonstrated to be more efficient than linking pre-docked peptide fragments. The new protocol combines the advantages of popular program packages and allows fast generation of solution structures. AutoDock, a force-field-based program, is used to supply the docked peptide fragments used as structural seeds, and the building algorithm of Modeller is adopted and tested as a peptide growing engine. The performance of PepGrow is compared to ten other docking methods, and it is concluded that in situ growing of a ligand from a seed is a viable strategy for the production of complex structures of histone peptides at atomic resolution.

Development and application of fragment-based de novo inhibitor design approaches against Plasmodium falciparum GST

CONTEXT

Modulation of disease progression is frequently started by identifying biochemical pathway catalyzed by biomolecule that is prone to inhibition by small molecular weight ligands. Such ligands (leads) can be obtained from natural resources or synthetic libraries. However, de novo design based on fragments assembly and optimization is showing increasing success. Plasmodium falciparum parasite depends on glutathione-S-transferase (PfGST) in buffering oxidative heme as an approach to resist some antimalarials. Therefore, PfGST is considered an attractive target for drug development. In this research, fragment-based approaches were used to design molecules that can fit to glutathione (GSH) binding site (G-site) of PfGST.

METHODS

The involved approaches build molecules from fragments that are either isosteric to GSH sub-moieties (ligand-based) or successfully docked to GSH binding sub-pockets (structure-based). Compared to reference GST inhibitor of S-hexyl GSH, ligands with improved rigidity, synthetic accessibility, and affinity to receptor were successfully designed. The method involves joining fragments to create ligands. The ligands were then explored using molecular docking, Cartesian coordinate's optimization, and simplified free energy determination as well as MD simulation and MMPBSA calculations. Several tools were used which include OPENEYE toolkit, Open Babel, Autodock Vina, Gromacs, and SwissParam server, and molecular mechanics force field of MMFF94 for optimization and CHARMM27 for MD simulation. In addition, in-house scripts written in Matlab were used to control fragments connection and automation of the tools.

Computational Fragment-Based Design of Phytochemical Derivatives as EGFR Inhibitors

Epidermal growth factor receptor (EGFR) is a potential target with disease modifying benefits against Alzheimer's disease (AD). Repurposing of FDA approved drugs against EGFR have shown beneficial effect against AD but are confined to quinazoline, quinoline and aminopyrimidines. Futuristically, the possibility of acquiring drug resistance mutation as seen in the case of cancer could also hamper AD treatment. To identify novel chemical scaffolds, we rooted on phytochemicals identified from plants such as Acorus calamus, Bacopa monnieri, Convolvulus pluricaulis, Tinospora cordifloia, and Withania somnifera that have well-established records in the treatment of brain disorders. The rationale was to mimic the biosynthetic metabolite extension process observed in the plants for synthesizing new phytochemical derivates. Thus, novel compounds were designed computationally by fragment-based method followed by extensive in silico analysis to pick potential phytochemical derivates. PCD1, 8 and 10 were predicted to have better blood brain barrier permeability. ADMET and SoM analysis suggested that these PCDs exhibited druglike properties. Further simulation studies showed that PCD1 and PCD8 stably interact with EGFR and have the potential to be used even in cases of drug-resistance mutations. With further experimental evidence, these PCDs could be leveraged as potential inhibitors of EGFR.

Advances of bioorthogonal coupling reactions in drug development

Currently, bioorthogonal coupling reactions have garnered considerable interest due to their high substrate selectivity and less restrictive reaction conditions. During recent decades, bioorthogonal coupling reactions have emerged as powerful tools in drug development. This review describes the current applications of bioorthogonal coupling reactions in compound library building mediated by the copper-catalyzed azide-alkyne cycloaddition (CuAAC) reaction and in situ click chemistry or conjunction with other techniques; druggability optimization with 1,2,3-triazole groups; and intracellular self-assembly platforms with ring tension reactions, which are presented from the viewpoint of drug development. There is a reasonable prospect that bioorthogonal coupling reactions will accelerate the screening of lead compounds, the designing strategies of small molecules and expand the variety of designed compounds, which will be a new trend in drug development in the future.

Application of computational methods for class A GPCR Ligand discovery

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.

DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning

Rational drug design often starts from specific scaffolds to which side chains/substituents are added or modified due to the large drug-like chemical space available to search for novel drug-like molecules. With the rapid growth of deep learning in drug discovery, a variety of effective approaches have been developed for de novo drug design. In previous work we proposed a method named DrugEx, which can be applied in polypharmacology based on multi-objective deep reinforcement learning. However, the previous version is trained under fixed objectives and does not allow users to input any prior information (i.e. a desired scaffold). In order to improve the general applicability, we updated DrugEx to design drug molecules based on scaffolds which consist of multiple fragments provided by users. Here, a  Transformer model was employed to generate molecular structures. The Transformer is a multi-head self-attention deep learning model containing an encoder to receive scaffolds as input and a decoder to generate molecules as output. In order to deal with the graph representation of molecules a novel positional encoding for each atom and bond based on an adjacency matrix was proposed, extending the architecture of the Transformer. The graph Transformer model contains growing and connecting procedures for molecule generation starting from  a given scaffold based on fragments. Moreover, the generator was trained under a reinforcement learning framework to increase the number of desired ligands. As a proof of concept, the method was applied to design ligands for the adenosine A_2A receptor (A_2AAR) and compared with SMILES-based methods. The results show that 100% of the generated molecules are valid and most of them had a high predicted affinity value towards A_2AAR with given scaffolds.

DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design

Fragment-based drug discovery is a widely used strategy for drug design in both academic and pharmaceutical industries. Although fragments can be linked to generate candidate compounds by the latest deep generative models, generating linkers with specified attributes remains underdeveloped. In this study, we presented a novel framework, DRlinker, to control fragment linking toward compounds with given attributes through reinforcement learning. The method has been shown to be effective for many tasks from controlling the linker length and log P, optimizing predicted bioactivity of compounds, to various multiobjective tasks. Specifically, our model successfully generated 91.0% and 93.9% of compounds complying with the desired linker length and log P and improved the 7.5 pChEMBL value in bioactivity optimization. Finally, a quasi-scaffold-hopping study revealed that DRlinker could generate nearly 30% molecules with high 3D similarity but low 2D similarity to the lead inhibitor, demonstrating the benefits and applicability of DRlinker in actual fragment-based drug design.

Target-Focused Library Design by Pocket-Applied Computer Vision and Fragment Deep Generative Linking

We here describe a computational approach (POEM: Pocket Oriented Elaboration of Molecules) to drive the generation of target-focused libraries while taking advantage of all publicly available structural information on protein-ligand complexes. A collection of 31 384 PDB-derived images with key shapes and pharmacophoric properties, describing fragment-bound microenvironments, is first aligned to the query target cavity by a computer vision method. The fragments of the most similar PDB subpockets are then directly positioned in the query cavity using the corresponding image transformation matrices. Lastly, suitable connectable atoms of oriented fragment pairs are linked by a deep generative model to yield fully connected molecules. POEM was applied to generate a library of 1.5 million potential cyclin-dependent kinase 8 inhibitors. By synthesizing and testing as few as 43 compounds, a few nanomolar inhibitors were quickly obtained with limited resources in just two iterative cycles.

Evaluating the use of absolute binding free energy in the fragment optimisation process

Key to the fragment optimisation process within drug design is the need to accurately capture the changes in affinity that are associated with a given set of chemical modifications. Due to the weakly binding nature of fragments, this has proven to be a challenging task, despite recent advancements in leveraging experimental and computational methods. In this work, we evaluate the use of Absolute Binding Free Energy (ABFE) calculations in guiding fragment optimisation decisions, retrospectively calculating binding free energies for 59 ligands across 4 fragment elaboration campaigns. We first demonstrate that ABFEs can be used to accurately rank fragment-sized binders with an overall Spearman's r of 0.89 and a Kendall τ of 0.67, although often deviating from experiment in absolute free energy values with an RMSE of 2.75 kcal/mol. We then also show that in several cases, retrospective fragment optimisation decisions can be supported by the ABFE calculations. Comparing against cheaper endpoint methods, namely Nwat-MM/GBSA, we find that ABFEs offer better ranking power and correlation metrics. Our results indicate that ABFE calculations can usefully guide fragment elaborations to maximise affinity.

Fragment-based design of SARS-CoV-2 Mpro inhibitors

The SARS-CoV-2 virus has been identified as a causative agent for COVID-19 pandemic. About more than 6.3 million fatalities have been attributed to COVID-19 worldwide to date. Finding a viable cure for the illness is urgently needed in light of the present pandemic. The prominence of main protease in the life cycle of virus shapes the main protease as a viable target for design and development of antiviral agents to combat COVID-19. The current study presents the fragment linking strategy to design the novel Mpro inhibitors for COVID-19. A total of 293,451 fragments from diversified libraries have been screened for their binding affinity towards Mpro enzyme. The best 1600 fragment hits were subjected to fragment joining to achieve 100 new molecules using Schrödinger software. The resulting molecules were further screened for their Mpro binding affinity, ADMET, and drug-likeness features. The best 13 molecules were selected, and the first 6 compounds were investigated for their ligand-receptor complex stability through a molecular dynamics study using GROMACS software. The resulting molecules have the potential to be further evaluated for COVID-19 drug discovery.

FastGrow: on-the-fly growing and its application to DYRK1A

Fragment-based drug design is an established routine approach in both experimental and computational spheres. Growing fragment hits into viable ligands has increasingly shifted into the spotlight. FastGrow is an application based on a shape search algorithm that addresses this challenge at high speeds of a few milliseconds per fragment. It further features a pharmacophoric interaction description, ensemble flexibility, as well as geometry optimization to become a fully fledged structure-based modeling tool. All features were evaluated in detail on a previously reported collection of fragment growing scenarios extracted from crystallographic data. FastGrow was also shown to perform competitively versus established docking software. A case study on the DYRK1A kinase, using recently reported new chemotypes, illustrates FastGrow's features in practice and its ability to identify active fragments. FastGrow is freely available to the public as a web server at https://fastgrow.plus/ and is part of the SeeSAR 3D software package.

Entrectinib, a new multi-target inhibitor for cancer therapy

Clinical practice shows that when single-target drugs treat multi-factor diseases such as tumors, cardiovascular system and endocrine system diseases, it is often difficult to achieve good therapeutic effects, and even serious adverse reactions may occur. Multi-target drugs can simultaneously regulate multiple links of disease, improve efficacy, reduce adverse reactions, and improve drug resistance. They are ideal drugs for treating complex diseases, and therefore have become the main direction of drug development. At present, some multi-target drugs have been successfully used in many major diseases. Entrectinib is an oral small molecule inhibitor that targets TRK, ROS1, and ALK. It is used to treat locally advanced or metastatic solid tumors with NTRK1/2/3, ROS1 and ALK gene fusion mutations. It can pass through the blood-brain barrier and is the only TRK inhibitor clinically proven to be effective against primary and metastatic brain diseases. In 2019, entrectinib was approved by the FDA to treat adult patients with ROS1-positive metastatic non-small cell lung cancer. Case reports showed that continuous administration of entrectinib was effective and tolerable. In this review, we give a brief introduction to TKK, ROS1 and ALK, and on this basis, we give a detailed and comprehensive introduction to the mechanism of action, pharmacokinetics, pharmacodynamics, clinical efficacy, tolerability and drug interactions of entrectinib.

SAMPL7 protein-ligand challenge: A community-wide evaluation of computational methods against fragment screening and pose-prediction

A novel crystallographic fragment screening data set was generated and used in the SAMPL7 challenge for protein-ligands. The SAMPL challenges prospectively assess the predictive power of methods involved in computer-aided drug design. Application of various methods to fragment molecules are now widely used in the search for new drugs. However, there is little in the way of systematic validation specifically for fragment-based approaches. We have performed a large crystallographic high-throughput fragment screen against the therapeutically relevant second bromodomain of the Pleckstrin-homology domain interacting protein (PHIP2) that revealed 52 different fragments bound across 4 distinct sites, 47 of which were bound to the pharmacologically relevant acetylated lysine (Kac) binding site. These data were used to assess computational screening, binding pose prediction and follow-up enumeration. All submissions performed randomly for screening. Pose prediction success rates (defined as less than 2 Å root mean squared deviation against heavy atom crystal positions) ranged between 0 and 25% and only a very few follow-up compounds were deemed viable candidates from a medicinal-chemistry perspective based on a common molecular descriptors analysis. The tight deadlines imposed during the challenge led to a small number of submissions suggesting that the accuracy of rapidly responsive workflows remains limited. In addition, the application of these methods to reproduce crystallographic fragment data still appears to be very challenging. The results show that there is room for improvement in the development of computational tools particularly when applied to fragment-based drug design.

Designing Novel Compounds for the Treatment and Management of RET-Positive Non-Small Cell Lung Cancer-Fragment Based Drug Design Strategy

Rearranged during transfection (RET) is an oncogenic driver receptor that is overexpressed in several cancer types, including non-small cell lung cancer. To date, only multiple kinase inhibitors are widely used to treat RET-positive cancer patients. These inhibitors exhibit high toxicity, less efficacy, and specificity against RET. The development of drug-resistant mutations in RET protein further deteriorates this situation. Hence, in the present study, we aimed to design novel drug-like compounds using a fragment-based drug designing strategy to overcome these issues. About 18 known inhibitors from diverse chemical classes were fragmented and bred to form novel compounds against RET proteins. The inhibitory activity of the resultant 115 hybrid molecules was evaluated using molecular docking and RF-Score analysis. The binding free energy and chemical reactivity of the compounds were computed using MM-GBSA and density functional theory analysis, respectively. The results from our study revealed that the developed hybrid molecules except for LF21 and LF27 showed higher reactivity and stability than Pralsetinib. Ultimately, the process resulted in three hybrid molecules namely LF1, LF2, and LF88 having potent inhibitory activity against RET proteins. The scrutinized molecules were then subjected to molecular dynamics simulation for 200 ns and MM-PBSA analysis to eliminate a false positive design. The results from our analysis hypothesized that the designed compounds exhibited significant inhibitory activity against multiple RET variants. Thus, these could be considered as potential leads for further experimental studies.

A Lead-Based Fragment Library Screening of the Glycosyltransferase WaaG from Escherichia coli

Glucosyl transferase I (WaaG) in E. coli catalyzes the transfer of an α-d-glucosyl group to the inner core of the lipopolysaccharide (LPS) and plays an important role in the biogenesis of the outer membrane. If its activity could be inhibited, the integrity of the outer membrane would be compromised and the bacterium would be susceptible to antibiotics that are normally prevented from entering the cell. Herein, three libraries of molecules (A, B and C) were docked in the binding pocket of WaaG, utilizing the docking binding affinity as a filter to select fragment-based compounds for further investigations. From the results of the docking procedure, a selection of compounds was investigated by molecular dynamics (MD) simulations to obtain binding free energy (BFE) and K_D values for ligands as an evaluation for the binding to WaaG. Derivatives of 1,3-thiazoles (A7 and A4) from library A and 1,3,4-thiadiazole (B33) from library B displayed a promising profile of BFE, with K_D < mM, viz., 0.11, 0.62 and 0.04 mM, respectively. Further root-mean-square-deviation (RMSD), electrostatic/van der Waals contribution to the binding and H-bond interactions displayed a favorable profile for ligands A4 and B33. Mannose and/or heptose-containing disaccharides C1–C4, representing sub-structures of the inner core of the LPS, were also investigated by MD simulations, and compound C4²⁻ showed a calculated K_D = 0.4 µM. In the presence of UDP-Glc²⁻, the best-docked pose of disaccharide C4²⁻ is proximate to the glucose-binding site of WaaG. A study of the variation in angle and distance was performed on the different portions of WaaG (N-, the C- domains and the hinge region). The Spearman correlation coefficient between the two variables was close to unity, where both variables increase in the same way, suggesting a conformational rearrangement of the protein during the MD simulation, revealing molecular motions of the enzyme that may be part of the catalytic cycle. Selected compounds were also analyzed by Saturation Transfer Difference (STD) NMR experiments. STD effects were notable for the 1,3-thiazole derivatives A4, A8 and A15 with the apo form of the protein as well as in the presence of UDP for A4.

Fragment based drug design

Rational approaches towards drug development have emerged as one of the most promising ways among the tedious conventional procedures with the aim of redefining the drug discovery process. The need of current medical system is demanding a much precise, faster and reliable approaches in parallel to faster growing technology for development of drugs with more intrinsic action and fewer side effects. Systematic and well-defined diagnostic studies have revealed the specific causes of disease and related targets for drug development. Designing a drug as per the specific target, studying it in-silico prior to its development has been proved as an added benefit to the studies. Many approaches like structure based drug design, fragment based drug design and ligand based drug design are been in practice for the drug discovery and development with the similar fundamental theory. Fragment based drug design utilizes a library of fragments designed specifically for the concerned target and these fragments are studied further before screening with virtual methods as well as with biophysical methods. The process follows a well-defined pathway which moulds a fragment into a perfect drug candidate. In this chapter we have tried to cover all the basic aspects of fragment based drug design and related technologies.

Design, synthesis, and biological evaluation of N-(3-cyano-1H-indol-5/6-yl)-6-oxo-1,6-dihydropyrimidine-4-carboxamides and 5-(6-oxo-1,6-dihydropyrimidin-2-yl)-1H-indole-3-carbonitriles as novel xanthine oxidase inhibitors

Xanthine oxidase (XO) has been an important target for the treatment of hyperuricemia and gout. The analysis of potential interactions of pyrimidinone and 3-cyano indole pharmacophores present in the corresponding reported XO inhibitors with parts of the XO active pocket indicated that they both can be used as effective fragments for the fragment-based design of nonpurine XO inhibitors. In this paper, we adopted the fragment-based drug design strategy to link the two fragments with an amide bond to design the type 1 compounds 13a-13w,14c, 14d, 14f, 14g, 14j, 14k, and 15g. Compound 13g displayed an evident XO inhibitory potency (IC₅₀ = 0.16 μM), which was 52.3-fold higher than that of allopurinol (IC₅₀ = 8.37 μM). For comparison, type 2 compounds 5-(6-oxo-1,6-dihydropyrimidin-2-yl)-1H-indole-3-carbonitriles (25c-25g) were also designed by linking the two fragments with a single bond directly. The results showed that compound 25c from the latter series displayed the best inhibitory potency (IC₅₀ = 0.085 μM), and it was 98.5-fold stronger than that of allopurinol (IC₅₀ = 8.37 μM). These results suggested that amide and single bonds were applicable for linking the two fragments together to obtain potent nonpurine XO inhibitors. The structure-activity relationship results revealed that hydrophobic groups at N-atom of the indole moiety were indispensable for the improvement of the inhibitory potency in vitro against XO. In addition, enzyme kinetics studies suggested that compounds 13g and 25c, as the most promising XO inhibitors for the two types of target compounds, acted as mixed-type inhibitors for XO. Moreover, molecular modeling studies suggested that the pyrimidinone and indole moieties of the target compounds could interact well with key amino acid residues in the active pocket of XO. Furthermore, in vivo hypouricemic effect demonstrated that compounds 13g and 25c could effectively reduce serum uric acid levels at an oral dose of 10 mg/kg. Therefore, compounds 13g and 25c could be potential and efficacious agents for the treatment of hyperuricemia and gout.

Construction and activity evaluation of novel dual-target (SE/CYP51) anti-fungal agents containing amide naphthyl structure

With the increase of fungal infection and drug resistance, it is becoming an urgent task to discover the highly effective antifungal drugs. In the study, we selected the key ergosterol bio-synthetic enzymes (Squalene epoxidase, SE; 14 α-demethylase, CYP51) as dual-target receptors to guide the construction of novel antifungal compounds, which could achieve the purpose of improving drug efficacy and reducing drug-resistance. Three different series of amide naphthyl compounds were generated through the method of skeleton growth, and their corresponding target products were synthesized. Most of compounds displayed the obvious biological activity against different Candida spp. and Aspergillus fumigatus. Among of them, target compounds 14a-2 and 20b-2 not only possessed the excellent broad-spectrum anti-fungal activity (MIC₅₀, 0.125-2 μg/mL), but also maintained the anti-drug-resistant fungal activity (MIC₅₀, 1-4 μg/mL). Preliminary mechanism study revealed the compounds (14a-2, 20b-2) could block the bio-synthetic pathway of ergosterol by inhibiting the dual-target (SE/CYP51) activity, and this finally caused the cleavage and death of fungal cells. In addition, we also discovered that compounds 14a-2 and 20b-2 with low toxic and side effects could exert the excellent therapeutic effect in mice model of fungal infection, which was worthy for further in-depth study.

Comprehensive Approach of 19F Nuclear Magnetic Resonance, Enzymatic, and In Silico Methods for Site-Specific Hit Selection and Validation of Fragment Molecules that Inhibit Methionine γ-Lyase Activity

Fragment-based screening using ¹⁹F NMR (¹⁹F-FS) is an efficient method for exploring seed and lead compounds for drug discovery. Here, we demonstrate the utility and merits of using ¹⁹F-FS for methionine γ-lyase-binding fragments, together with a ¹⁹F NMR-based competition and mutation assay, as well as enzymatic and in silico methods. ¹⁹F NMR-based assays provided useful information on binding between ¹⁹F-FS hit fragments and target proteins. Although the ¹⁹F-FS and enzymatic assay were weakly correlated, they show that the ¹⁹F-FS hit fragments contained compounds with inhibitory activity. Furthermore, we found that in silico calculations partially account for the differences in activity levels between the ¹⁹F-FS hits as per NMR analysis. A comprehensive approach combining the ¹⁹F-FS and other methods not only identified fragment hits but also distinguished structural differences in chemical groups with diverse activity levels.

Comprehensive Survey of Recent Drug Discovery Using Deep Learning

Drug discovery based on artificial intelligence has been in the spotlight recently as it significantly reduces the time and cost required for developing novel drugs. With the advancement of deep learning (DL) technology and the growth of drug-related data, numerous deep-learning-based methodologies are emerging at all steps of drug development processes. In particular, pharmaceutical chemists have faced significant issues with regard to selecting and designing potential drugs for a target of interest to enter preclinical testing. The two major challenges are prediction of interactions between drugs and druggable targets and generation of novel molecular structures suitable for a target of interest. Therefore, we reviewed recent deep-learning applications in drug-target interaction (DTI) prediction and de novo drug design. In addition, we introduce a comprehensive summary of a variety of drug and protein representations, DL models, and commonly used benchmark datasets or tools for model training and testing. Finally, we present the remaining challenges for the promising future of DL-based DTI prediction and de novo drug design.

CASTELO: clustered atom subtypes aided lead optimization-a combined machine learning and molecular modeling method

Background

Drug discovery is a multi-stage process that comprises two costly major steps: pre-clinical research and clinical trials. Among its stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose a combined machine learning and molecular modeling approach that partially automates lead optimization workflow in silico, providing suggestions for modification hot spots.

Results

The initial data collection is achieved with physics-based molecular dynamics simulation. Contact matrices are calculated as the preliminary features extracted from the simulations. To take advantage of the temporal information from the simulations, we enhanced contact matrices data with temporal dynamism representation, which are then modeled with unsupervised convolutional variational autoencoder (CVAE). Finally, conventional and CVAE-based clustering methods are compared with metrics to rank the submolecular structures and propose potential candidates for lead optimization.

Conclusion

With no need for extensive structure-activity data, our method provides new hints for drug modification hotspots which can be used to improve drug potency and reduce the lead optimization time. It can potentially become a valuable tool for medicinal chemists.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04214-4.

Use of molecular docking computational tools in drug discovery

Molecular docking has become an important component of the drug discovery process. Since first being developed in the 1980s, advancements in the power of computer hardware and the increasing number of and ease of access to small molecule and protein structures have contributed to the development of improved methods, making docking more popular in both industrial and academic settings. Over the years, the modalities by which docking is used to assist the different tasks of drug discovery have changed. Although initially developed and used as a standalone method, docking is now mostly employed in combination with other computational approaches within integrated workflows. Despite its invaluable contribution to the drug discovery process, molecular docking is still far from perfect. In this chapter we will provide an introduction to molecular docking and to the different docking procedures with a focus on several considerations and protocols, including protonation states, active site waters and consensus, that can greatly improve the docking results.

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises

There is an urgent need to identify new therapies that prevent SARS-CoV-2 infection and improve the outcome of COVID-19 patients. This pandemic has thus spurred intensive research in most scientific areas and in a short period of time, several vaccines have been developed. But, while the race to find vaccines for COVID-19 has dominated the headlines, other types of therapeutic agents are being developed. In this mini-review, we report several databases and online tools that could assist the discovery of anti-SARS-CoV-2 small chemical compounds and peptides. We then give examples of studies that combined in silico and in vitro screening, either for drug repositioning purposes or to search for novel bioactive compounds. Finally, we question the overall lack of discussion and plan observed in academic research in many countries during this crisis and suggest that there is room for improvement.

Fragment binding to the Nsp3 macrodomain of SARS-CoV-2 identified through crystallographic screening and computational docking

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) macrodomain within the nonstructural protein 3 counteracts host-mediated antiviral adenosine diphosphate-ribosylation signaling. This enzyme is a promising antiviral target because catalytic mutations render viruses nonpathogenic. Here, we report a massive crystallographic screening and computational docking effort, identifying new chemical matter primarily targeting the active site of the macrodomain. Crystallographic screening of 2533 diverse fragments resulted in 214 unique macrodomain-binders. An additional 60 molecules were selected from docking more than 20 million fragments, of which 20 were crystallographically confirmed. X-ray data collection to ultra-high resolution and at physiological temperature enabled assessment of the conformational heterogeneity around the active site. Several fragment hits were confirmed by solution binding using three biophysical techniques (differential scanning fluorimetry, homogeneous time-resolved fluorescence, and isothermal titration calorimetry). The 234 fragment structures explore a wide range of chemotypes and provide starting points for development of potent SARS-CoV-2 macrodomain inhibitors.

Fragment-Based Drug Design of Selective HDAC6 Inhibitors

Medicinal chemistry society has enough arguments to justify the usage of fragment-based drug design (FBDD) methodologies for the identification of lead compounds. Since the FDA approval of three kinase inhibitors - vemurafenib, venetoclax, and erdafitinib, FBDD has become a challenging alternative to high-throughput screening methods in drug discovery. The following protocol presents in silico drug design of selective histone deacetylase 6 (HDAC6) inhibitors through a fragment-based approach. To date, structural motifs that are important for HDAC inhibitory activity and selectivity are described as: surface recognition group (CAP group), aliphatic or aromatic linker, and zinc-binding group (ZBG). The main idea of this FBDD method is to identify novel and target-selective CAP groups by virtual scanning of publicly available fragment databases. Template structure used to search for novel heterocyclic and carbocyclic fragments is 1,8-naphthalimide (CAP group of scriptaid, a potent HDAC inhibitor). Herein, the design of HDAC6 inhibitors is based on linking the identified fragments with the aliphatic or aromatic linker and hydroxamic acid (ZBG) moiety. Final selection of potential selective HDAC6 inhibitors is based on combined structure-based (molecular docking) and ligand-based (three-dimensional quantitative structure-activity relationships, 3D-QSAR) techniques. Designed compounds are docked in the active site pockets of human HDAC1 and HDAC6 isoforms, and their docking conformations used to predict their HDAC inhibitory and selectivity profiles through two developed 3D-QSAR models (describing HDAC1 and HDAC6 inhibitory activities).

Direct Keap1-kelch inhibitors as potential drug candidates for oxidative stress-orchestrated diseases: A review on In silico perspective

The recent outcry in the search for direct keap1 inhibitors requires a quicker and more effective drug discovery process which is an inherent property of the Computer Aided Drug Discovery (CADD) to bring drug candidates into the clinic for patient's use. This Keap1 (negative regulator of ARE master activator) is emerging as a therapeutic strategy to combat oxidative stress-orchestrated diseases. The advances in computer algorithm and compound databases require that we highlight the functionalities that this technology possesses that can be exploited to target Keap1-Nrf2 PPI. Therefore, in this review, we uncover the in silico approaches that had been exploited towards the identification of keap1 inhibition in the light of appropriate fitting with relevant amino acid residues, we found 3 and 16 other compounds that perfectly fit keap1 kelch pocket/domain. Our goal is to harness the parameters that could orchestrate keap1 surface druggability by utilizing hotspot regions for virtual fragment screening and identification of hotspot residues.

Discovering High Potent Hsp90 Inhibitors as Antinasopharyngeal Carcinoma Agents through Fragment Assembling Approach

Hsp90 is a new promising target for cancer treatment. Many inhibitors have been discovered as therapeutic agents, and some have passed Phase I and II. However, no one is approved by FDA yet. Novel and druggable Hsp90 inhibitors are still demanding. Here, we report a new way to discover high potent Hsp90 inhibitors as antinasopharyngeal carcinoma agents through assembling fragments. With chemotyping analysis, we extract seven chemotypes from 3482 known Hsp90 inhibitors, screen 500 fragments that are compatible with the chemotypes, and confirm 15 anti-Hsp90 fragments. Click chemistry is employed to construct 172 molecules and synthesize 21 compounds among them. The best inhibitor 3d was further optimized and resulted in more potent 4f (IC50 = 0.16 μM). In vitro and in vivo experiments confirmed that 4f is a promising agent against nasopharyngeal carcinoma. This study may provide a strategy in discovering new ligands against targets without well-understood structures.

Generative chemistry: drug discovery with deep learning generative models

The de novo design of molecular structures using deep learning generative models introduces an encouraging solution to drug discovery in the face of the continuously increased cost of new drug development. From the generation of original texts, images, and videos, to the scratching of novel molecular structures the creativity of deep learning generative models exhibits the height machine intelligence can achieve. The purpose of this paper is to review the latest advances in generative chemistry which relies on generative modeling to expedite the drug discovery process. This review starts with a brief history of artificial intelligence in drug discovery to outline this emerging paradigm. Commonly used chemical databases, molecular representations, and tools in cheminformatics and machine learning are covered as the infrastructure for generative chemistry. The detailed discussions on utilizing cutting-edge generative architectures, including recurrent neural network, variational autoencoder, adversarial autoencoder, and generative adversarial network for compound generation are focused. Challenges and future perspectives follow.

LEADS-FRAG: A Benchmark Data Set for Assessment of Fragment Docking Performance

Fragment-based drug design is a popular approach in drug discovery, which makes use of computational methods such as molecular docking. To assess fragment placement performance of molecular docking programs, we constructed LEADS-FRAG, a benchmark data set containing 93 high-quality protein-fragment complexes that were selected from the Protein Data Bank using a rational and unbiased process. The data set contains fully prepared protein and fragment structures and is publicly available. Moreover, we used LEADS-FRAG for evaluating the small-molecule docking programs AutoDock, AutoDock Vina, FlexX, and GOLD for their fragment docking performance. GOLD in combination with the scoring function ChemPLP and AutoDock Vina performed best and generated near-native conformations (root mean square deviation <1.5 Å) for more than 50% of the data set considering the top-ranked docking pose. Taking into account all docking poses, the tested programs generated near-native conformations for up to 86% of the fragments in LEADS-FRAG. By rescoring all docking poses with the GOLD scoring functions and the Protein-Ligand Informatics force field, the number of near-native conformations increased up to 40% with respect to the top-rescored poses. Our results show that conventional small-molecule docking programs achieve a satisfactory fragment docking performance when utilizing rescoring.

Non-hydroxamate inhibitors of 1-deoxy-d-xylulose 5-phosphate reductoisomerase (DXR): A critical review and future perspective

1-deoxy-d-xylulose 5-phosphate reductoisomerase (DXR) catalyzes the second step of the non-mevalonate (or MEP) pathway that functions in several organisms and plants for the synthesis of isoprenoids. DXR is essential for the survival of multiple pathogenic bacteria/parasites, including those that cause tuberculosis and malaria in humans. DXR function is inhibited by fosmidomycin (1), a natural product, which forms a chelate with the active site divalent metal (Mg²⁺/Mn²⁺) through its hydroxamate metal-binding group (MBG). Most of the potent DXR inhibitors are structurally similar to 1 and retain hydroxamate despite the unfavourable pharmacokinetic and toxicity profile of the latter. We provide our perspective on the lack of non-hydroxamate DXR inhibitors. We also highlight the fundamental flaws in the design of MBG in these molecules, primarily responsible for their failure to inhibit DXR. We also suggest that for designing next-generation non-hydroxamate DXR inhibitors, approaches followed for other metalloenzymes targets may be exploited.

Fragment Binding to the Nsp3 Macrodomain of SARS-CoV-2 Identified Through Crystallographic Screening and Computational Docking

The SARS-CoV-2 macrodomain (Mac1) within the non-structural protein 3 (Nsp3) counteracts host-mediated antiviral ADP-ribosylation signalling. This enzyme is a promising antiviral target because catalytic mutations render viruses non-pathogenic. Here, we report a massive crystallographic screening and computational docking effort, identifying new chemical matter primarily targeting the active site of the macrodomain. Crystallographic screening of diverse fragment libraries resulted in 214 unique macrodomain-binding fragments, out of 2,683 screened. An additional 60 molecules were selected from docking over 20 million fragments, of which 20 were crystallographically confirmed. X-ray data collection to ultra-high resolution and at physiological temperature enabled assessment of the conformational heterogeneity around the active site. Several crystallographic and docking fragment hits were validated for solution binding using three biophysical techniques (DSF, HTRF, ITC). Overall, the 234 fragment structures presented explore a wide range of chemotypes and provide starting points for development of potent SARS-CoV-2 macrodomain inhibitors.

Covalent allosteric modulation: An emerging strategy for GPCRs drug discovery

Designing covalent allosteric modulators brings new opportunities to the field of drug discovery towards G-protein-coupled receptors (GPCRs). Targeting an allosteric binding pocket can allow a modulator to have protein subtype selectivity and low drug resistance. Utilizing covalent warheads further enables the modulator to increase the binding potency and extend the duration of action. This review starts with GPCR allosteric modulation to discuss the structural biology of allosteric binding pockets, the different types of allosteric modulators, as well as the advantages of employing allosteric modulation. This is followed by a discussion on covalent modulators to clarify how covalent ligands can benefit the receptor modulation and to illustrate moieties that can commonly be used as covalent warheads. Finally, case studies are presented on designing class A, B, and C GPCR covalent allosteric modulators to demonstrate successful stories on combining allosteric modulation and covalent binding. Limitations and future perspectives are also covered.

High-Throughput Screening: today's biochemical and cell-based approaches

High-throughput screening (HTS) provides starting chemical matter in the adventure of developing a new drug. In this review, we survey several HTS methods used today for hit identification, organized in two main flavors: biochemical and cell-based assays. Biochemical assays discussed include fluorescence polarization and anisotropy, FRET, TR-FRET, and fluorescence lifetime analysis. Binding-based methods are also surveyed, including NMR, SPR, mass spectrometry, and DSF. On the other hand, cell-based assays discussed include viability, reporter gene, second messenger, and high-throughput microscopy assays. We devote some emphasis to high-content screening, which is becoming very popular. An advisable stage after hit discovery using phenotypic screens is target deconvolution, and we provide an overview of current chemical proteomics, in silico, and chemical genetics tools. Emphasis is made on recent CRISPR/dCas-based screens. Lastly, we illustrate some of the considerations that inform the choice of HTS methods and point to some areas with potential interest for future research.