The rise of fragment-based drug discovery

Comprehensive analysis of commercial fragment libraries

Screening of fragment libraries is a valuable approach to the drug discovery process. The quality of the library is one of the keys to success, and more particularly the design or choice of a library has to meet the specificities of the research program. In this study, we made an inventory of the commercial fragment libraries and we established a methodology which allows any library to be positioned in relation to the complete offer currently on the market, by addressing the following questions: does this chemical library look like another chemical library? What is the coverage of the current chemical space by this chemical library? What are the characteristic structural features of the fragments of this chemical library? We based our analysis on 2D and 3D chemical descriptors, framework class generation and the generative topographic map. We identified 59 270 scaffolds, which can be searched in a dedicated web site (https://gtmfrag.drugdesign.unistra.fr) and developed a model which accounts for fragment diversity while being easy to interpret (download at 10.5281/zenodo.5534434).

In Silico Structure-Based Approach for Group Efficiency Estimation in Fragment-Based Drug Design Using Evaluation of Fragment Contributions

The notion of a contribution of a specific group in an organic molecule's property and/or activity is both common in our thinking and is still not strictly correct due to the inherent non-additivity of free energy with respect to molecular fragments composing a molecule. The fragment- based drug discovery (FBDD) approach has proven to be fruitful in addressing the above notions. The main difficulty of the FBDD, however, is in its reliance on the low throughput and expensive experimental means of determining the fragment-sized molecules binding. In this article we propose a way to enhance the throughput and availability of the FBDD methods by judiciously using an in silico means of assessing the contribution to ligand-receptor binding energy of fragments of a molecule under question using a previously developed in silico Reverse Fragment Based Drug Discovery (R-FBDD) approach. It has been shown that the proposed structure-based drug discovery (SBDD) type of approach fills in the vacant niche among the existing in silico approaches, which mainly stem from the ligand-based drug discovery (LBDD) counterparts. In order to illustrate the applicability of the approach, our work retrospectively repeats the findings of the use case of an FBDD hit-to-lead project devoted to the experimentally based determination of additive group efficiency (GE)-an analog of ligand efficiency (LE) for a group in the molecule-using the Free-Wilson (FW) decomposition. It is shown that in using our in silico approach to evaluate fragment contributions of a ligand and to estimate GE one can arrive at similar decisions as those made using the experimentally determined activity-based FW decomposition. It is also shown that the approach is rather robust to the choice of the scoring function, provided the latter demonstrates a decent scoring power. We argue that the proposed approach of in silico assessment of GE has a wider applicability domain and expect that it will be widely applicable to enhance the net throughput of drug discovery based on the FBDD paradigm.

Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and "promiscuous" interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.

Visible-light induced dearomatization reactions

Dearomatization reactions provide rapid access to structurally complex three-dimensional molecules from simple aromatic compounds. Plenty of reports have demonstrated their utilities in the synthesis of natural products, medicinal chemistry, and materials science in the last decades. Recently, visible-light mediated photocatalysis has emerged as a powerful tool to promote many kinds of transformations. The dearomatization reactions induced by visible-light have also made significant progress during the past several years. This review provides an overview of visible-light induced dearomatization reactions classified based on the manner in which aromaticity is disrupted.

Engineered protein-small molecule conjugates empower selective enzyme inhibition

Potent, specific ligands drive precision medicine and fundamental biology. Proteins, peptides, and small molecules constitute effective ligand classes. Yet greater molecular diversity would aid the pursuit of ligands to elicit precise biological activity against challenging targets. We demonstrate a platform to discover protein-small molecule (PriSM) hybrids to combine unique pharmacophore activities and shapes with constrained, efficiently engineerable proteins. In this platform, a fibronectin protein library is displayed on yeast with a single cysteine coupled to acetazolamide via a maleimide-poly(ethylene glycol) linker. Magnetic and flow cytometric sorts enrich specific binders to carbonic anhydrase isoforms. Isolated PriSMs exhibit potent, specific inhibition of carbonic anhydrase isoforms with efficacy superior to that of acetazolamide or protein alone, including an 80-fold specificity increase and 9-fold potency gain. PriSMs are engineered with multiple linker lengths, protein conjugation sites, and sequences against two different isoforms, which reveal platform flexibility and impacts of molecular designs. PriSMs advance the molecular diversity of efficiently engineerable ligands.

Identification of a small compound that specifically inhibits Zika virus in vitro and in vivo by targeting the NS2B-NS3 protease

Zika virus (ZIKV) has rapid become a global threat, but no ZIKV-specific vaccines or drugs are currently available. In this study, inhibitors of ZIKV NS2B-NS3 protease were screened from a library containing 4,452 compound fragments. One of the compounds, 6-bromo-1,2-naphthalenedione, exhibited high specific inhibition against ZIKV NS2B-NS3 protease, but had no inhibitory effects against other viral proteases. A microscale thermophoresis (MST) assay confirmed that the compound bound to ZIKV NS2B-NS3 protein with a binding constant (Kd) of 12.26 μM. Indirect immunofluorescence assays, Western blots, and plaque assays indicated that the compound inhibited virus replication in cells. Virus titer was reduced by more than 75% when the compound was present at 1 μM. A time-of-addition assay showed that inhibition occurred at the virus replication stage, but not at the adsorption or invasion stages. The half cytotoxicity concentration (CC50) of the compound on HeLa, Vero, and BHK-21 cells were 445.44 μM, 123.87 μM, and 123.64 μM, respectively. In vivo tests using infected AG129 mice demonstrated that treatment with the compound reduced mortality by up to 60%. Mice treated with the compound showed a reduction in histopathological lesions in brain, testis, and ovary. Viral RNA, IL-1β, and IL-6 mRNA levels decreased significantly in these tissues. In summary, this study has identified a small compound with high and specific inhibitory effects on ZIKV. The compound can be used as a therapeutic agent and is also an ideal starting point for drug optimization.

Mapping oncogenic protein interactions for precision medicine

Normal protein‐protein interactions (normPPIs) occur with high fidelity to regulate almost every physiological process. In cancer, this highly organised and precisely regulated network is disrupted, hijacked or reprogrammed resulting in oncogenic protein‐protein interactions (oncoPPIs). OncoPPIs, which can result from genomic alterations, are a hallmark of many types of cancers. Recent technological advances in the field of mass spectrometry (MS)‐based interactomics, structural biology and drug discovery have prompted scientists to identify and characterise oncoPPIs. Disruption of oncoPPI interfaces has become a major focus of drug discovery programs and has resulted in the use of PPI‐specific drugs clinically. However, due to several technical hurdles, studies to build a reference oncoPPI map for various cancer types have not been undertaken. Therefore, there is an urgent need for experimental workflows to overcome the existing challenges in studying oncoPPIs in various cancers and to build comprehensive reference maps. Here, we discuss the important hurdles for characterising oncoPPIs and propose a three‐phase multidisciplinary workflow to identify and characterise oncoPPIs. Systematic identification of cancer‐type‐specific oncogenic interactions will spur new opportunities for PPI‐focused drug discovery projects and precision medicine.

Synthesis of pyrimidine-containing alkaloids

This review deals with the synthesis of naturally occurring alkaloids containing partially or completely saturated pyrimidine nuclei. The interest in these compounds is associated with their structural diversity, high biological activity and toxicity. The review is divided into four parts, each of which describes a number of synthetic methodologies toward structurally different naturally occurring alkaloids containing saturated cyclic six-membered amidine, guanidine, aminal and urea (thiourea) moieties, respectively. The development of various synthetic strategies for the preparation of these compounds has remarkably increased during the past few decades. This is primarily due to the fact that some of these compounds are isolated only in limited quantities, which makes it practically impossible to study their full structural characteristics and biological activity.

Quadruple Target Evaluation of Diversity-Optimized Halogen-Enriched Fragments (HEFLibs) Reveals Substantial Ligand Efficiency for AP2-Associated Protein Kinase 1 (AAK1)

Fragment-based drug discovery is one of the most utilized approaches for the identification of novel weakly binding ligands, by efficiently covering a wide chemical space with rather few compounds and by allowing more diverse binding modes to be found. This approach has led to various clinical candidates and approved drugs. Halogen bonding, on the other hand, has gained traction in molecular design and lead optimization, but could offer additional benefits in early drug discovery. Screening halogen-enriched fragments (HEFLibs) could alleviate problems associated with the late introduction of such a highly geometry dependent interaction. Usually, the binding mode is then already dominated by other strong interactions. Due to the fewer competing interactions in fragments, the halogen bond should more often act as an anchor point for the binding mode. Previously, we proposed a fragment library with a focus on diverse binding modes that involve halogens for gaining initial affinity and selectivity. Herein, we demonstrate the applicability of these HEFLibs with a small set of diverse enzymes: the histone-lysine N-methyltransferase DOT1L, the indoleamine 2,3-dioxygenase 1 (IDO1), the AP2-associated protein kinase 1 (AAK1), and the calcium/calmodulin-dependent protein kinase type 1G (CAMK1G). We were able to identify various binding fragments via STD-NMR. Using ITC to verify these initial hits, we determined affinities for many of these fragments. The best binding fragments exhibit affinities in the one-digit micromolar range and ligand efficiencies up to 0.83 for AAK1. A small set of analogs was used to study structure-affinity relationships and hereby analyze the specific importance of each polar interaction. This data clearly suggests that the halogen bond is the most important interaction of fragment 9595 with AAK1.

Medicines for millions of patients

In this opinion piece I share personal anecdotes from three drug discovery projects, sugammadex an anaesthetic reversal agent from Organon Scotland, and ribociclib and erdafitinib, both oncology drugs arising from Astex UK collaborations with Novartis and Janssen respectively. These drugs have been used to treat millions of patients. The learnings from this research focus on innovation, teamwork, and collaborations. Drug discovery, even with its frustrations and disappointments can be a great career for scientists in industry, in academia, or in a not-for-profit institute, who want their research to alleviate human suffering.

Fragment-to-Lead Medicinal Chemistry Publications in 2020

Fragment-based drug discovery (FBDD) continues to evolve and make an impact in the pharmaceutical sciences. We summarize successful fragment-to-lead studies that were published in 2020. Having systematically analyzed annual scientific outputs since 2015, we discuss trends and best practices in terms of fragment libraries, target proteins, screening technologies, hit-optimization strategies, and the properties of hit fragments and the leads resulting from them. As well as the tabulated Fragment-to-Lead (F2L) programs, our 2020 literature review identifies several trends and innovations that promise to further increase the success of FBDD. These include developing structurally novel screening fragments, improving fragment-screening technologies, using new computer-aided design and virtual screening approaches, and combining FBDD with other innovative drug-discovery technologies.

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.

Implementation of an AI-assisted fragment-generator in an open-source platform

We recently reported a deep learning model to facilitate fragment library design, which is critical for efficient hit identification. However, our model was implemented in Python. We have now created an implementation in the KNIME graphical pipelining environment which we hope will allow experimentation by users with limited programming knowledge.

We report a deep learning model to facilitate fragment library design, which is critical for efficient hit identification, and an implementation in the KNIME graphical workflow environment which should facilitate a more codeless use.

Identification and characterization of two drug-like fragments that bind to the same cryptic binding pocket of Burkholderia pseudomallei DsbA

Disulfide-bond-forming proteins (Dsbs) play a crucial role in the pathogenicity of many Gram-negative bacteria. Disulfide-bond-forming protein A (DsbA) catalyzes the formation of the disulfide bonds necessary for the activity and stability of multiple substrate proteins, including many virulence factors. Hence, DsbA is an attractive target for the development of new drugs to combat bacterial infections. Here, two fragments, bromophenoxy propanamide (1) and 4-methoxy-N-phenylbenzenesulfonamide (2), were identified that bind to DsbA from the pathogenic bacterium Burkholderia pseudomallei, the causative agent of melioidosis. The crystal structures of oxidized B. pseudomallei DsbA (termed BpsDsbA) co-crystallized with 1 or 2 show that both fragments bind to a hydrophobic pocket that is formed by a change in the side-chain orientation of Tyr110. This conformational change opens a `cryptic' pocket that is not evident in the apoprotein structure. This binding location was supported by 2D-NMR studies, which identified a chemical shift perturbation of the Tyr110 backbone amide resonance of more than 0.05 p.p.m. upon the addition of 2 mM fragment 1 and of more than 0.04 p.p.m. upon the addition of 1 mM fragment 2. Although binding was detected by both X-ray crystallography and NMR, the binding affinity (Kd) for both fragments was low (above 2 mM), suggesting weak interactions with BpsDsbA. This conclusion is also supported by the crystal structure models, which ascribe partial occupancy to the ligands in the cryptic binding pocket. Small fragments such as 1 and 2 are not expected to have a high energetic binding affinity due to their relatively small surface area and the few functional groups that are available for intermolecular interactions. However, their simplicity makes them ideal for functionalization and optimization. The identification of the binding sites of 1 and 2 to BpsDsbA could provide a starting point for the development of more potent novel antimicrobial compounds that target DsbA and bacterial virulence.

Discovery of Cofactor Competitive Inhibitors against the Human Methyltransferase Fibrillarin

Fibrillarin (FBL) is an essential and evolutionarily highly conserved S-adenosyl methionine (SAM) dependent methyltransferase. It is the catalytic component of a multiprotein complex that facilitates 2'-O-methylation of ribosomal RNAs (rRNAs), a modification essential for accurate and efficient protein synthesis in eukaryotic cells. It was recently established that human FBL (hFBL) is critical for Nipah, Hendra, and respiratory syncytial virus infections. In addition, overexpression of hFBL contributes towards tumorgenesis and is associated with poor survival in patients with breast cancer, suggesting that hFBL is a potential target for the development of both antiviral and anticancer drugs. An attractive strategy to target cofactor-dependent enzymes is the selective inhibition of cofactor binding, which has been successful for the development of inhibitors against several protein methyltransferases including PRMT5, DOT1L, and EZH2. In this work, we solved crystal structures of the methyltransferase domain of hFBL in apo form and in complex with the cofactor SAM. Screening of a fluorinated fragment library, via X-ray crystallography and 19F NMR spectroscopy, yielded seven hit compounds that competed with cofactor binding, two of which resulted in co-crystal structures. One of these structures revealed unexpected conformational variability in the cofactor binding site, which allows it to accommodate a compound significantly different from SAM. Our structural data provide critical information for the design of selective cofactor competitive inhibitors targeting hFBL, and preliminary elaboration of hit compounds has led to additional cofactor site binders.

Selection of Promising Novel Fragment Sized S. aureus SrtA Noncovalent Inhibitors Based on QSAR and Docking Modeling Studies

Sortase A (SrtA) of Staphylococcus aureus has been identified as a promising target to a new type of antivirulent drugs, and therefore, the design of lead molecules with a low nanomolar range of activity and suitable drug-like properties is important. In this work, we aimed at identifying new fragment-sized starting points to design new noncovalent S. aureus SrtA inhibitors by making use of the dedicated molecular motif, 5-arylpyrrolidine-2-carboxylate, which has been previously shown to be significant for covalent binding SrtA inhibitors. To this end, an in silico approach combining QSAR and molecular docking studies was used. The known SrtA inhibitors from the ChEMBL database with diverse scaffolds were first employed to derive descriptors and interpret their significance and correlation to activity. Then, the classification and regression QSAR models were built, which were used for rough ranking of the virtual library of the synthetically feasible compounds containing the dedicated motif. Additionally, the virtual library compounds were docked into the “activated” model of SrtA (PDB:2KID). The consensus ranking of the virtual library resulted in the most promising structures, which will be subject to further synthesis and experimental testing in order to establish new fragment-like molecules for further development into antivirulent drugs.

HT-SuMD: making molecular dynamics simulations suitable for fragment-based screening. A comparative study with NMR

Fragment-based lead discovery (FBLD) is one of the most efficient methods to develop new drugs. We present here a new computational protocol called High-Throughput Supervised Molecular Dynamics (HT-SuMD), which makes it possible to automatically screen up to thousands of fragments, representing therefore a new valuable resource to prioritise fragments in FBLD campaigns. The protocol was applied to Bcl-XL, an oncological protein target involved in the regulation of apoptosis through protein-protein interactions. Initially, HT-SuMD performances were validated against a robust NMR-based screening, using the same set of 100 fragments. These independent results showed a remarkable agreement between the two methods. Then, a virtual screening on a larger library of additional 300 fragments was carried out and the best hits were validated by NMR. Remarkably, all the in silico selected fragments were confirmed as Bcl-XL binders. This represents, to date, the largest computational fragments screening entirely based on MD.

A Deep Generative Model for Molecule Optimization via One Fragment Modification

Molecule optimization is a critical step in drug development to improve desired properties of drug candidates through chemical modification. We developed a novel deep generative model Modof over molecular graphs for molecule optimization. Modof modifies a given molecule through the prediction of a single site of disconnection at the molecule and the removal and/or addition of fragments at that site. A pipeline of multiple, identical Modof models is implemented into Modof-pipe to modify an input molecule at multiple disconnection sites. Here we show that Modof-pipe is able to retain major molecular scaffolds, allow controls over intermediate optimization steps and better constrain molecule similarities. Modof-pipe outperforms the state-of-the-art methods on benchmark datasets: without molecular similarity constraints, Modof-pipe achieves 81.2% improvement in octanol-water partition coefficient penalized by synthetic accessibility and ring size; and 51.2%, 25.6% and 9.2% improvement if the optimized molecules are at least 0.2, 0.4 and 0.6 similar to those before optimization, respectively. Modof-pipe is further enhanced into Modof-pipe m to allow modifying one molecule to multiple optimized ones. Modof-pipe m achieves additional performance improvement as at least 17.8% better than Modof-pipe.

NMR Spectroscopy of the Main Protease of SARS-CoV-2 and Fragment-Based Screening Identify Three Protein Hotspots and an Antiviral Fragment

The main protease (3CLp) of the SARS-CoV-2, the causative agent for the COVID-19 pandemic, is one of the main targets for drug development. To be active, 3CLp relies on a complex interplay between dimerization, active site flexibility, and allosteric regulation. The deciphering of these mechanisms is a crucial step to enable the search for inhibitors. In this context, using NMR spectroscopy, we studied the conformation of dimeric 3CLp from the SARS-CoV-2 and monitored ligand binding, based on NMR signal assignments. We performed a fragment-based screening that led to the identification of 38 fragment hits. Their binding sites showed three hotspots on 3CLp, two in the substrate binding pocket and one at the dimer interface. F01 is a non-covalent inhibitor of the 3CLp and has antiviral activity in SARS-CoV-2 infected cells. This study sheds light on the complex structure-function relationships of 3CLp and constitutes a strong basis to assist in developing potent 3CLp inhibitors.

The Fellowship of Privileged Scaffolds-One Structure to Inhibit Them All

Over the past few years, the application of privileged structure has emerged as a powerful approach to the discovery of new biologically active molecules. Privileged structures are molecular scaffolds with binding properties to the range of different biological targets. Moreover, privileged structures typically exhibit good drug-like properties, thus assuring more drug-like properties of modified compound. Our main objective is to discuss the privileged structures used for the development of antiviral agents.

Specific Inhibitor of Placental Alkaline Phosphatase Isolated from a DNA-Encoded Chemical Library Targets Tumor of the Female Reproductive Tract

Placental alkaline phosphatase (PLAP) is an abundant surface antigen in the malignancies of the female reproductive tract. Nevertheless, the discovery of PLAP-specific small organic ligands for targeting applications has been hindered by ligand cross-reactivity with the ubiquitous tissue non-specific alkaline phosphatase (TNAP). In this study, we used DNA-encoded chemical libraries to discover a potent (IC₅₀ = 32 nM) and selective PLAP inhibitor, with no detectable inhibition of TNAP activity. Subsequently, the PLAP ligand was conjugated to fluorescein; it specifically bound to PLAP-positive tumors in vitro and targeted cervical cancer in vivo in a mouse model of the disease. Ultimately, the fluorescent derivative of the PLAP inhibitor functioned as a bispecific engager redirecting the killing of chimeric antigen receptor-T cells specific to fluorescein on PLAP-positive tumor cells.

Inhibitors of Human Divalent Metal Transporters DMT1 (SLC11A2) and ZIP8 (SLC39A8) from a GDB-17 Fragment Library

Solute carrier proteins (SLCs) are membrane proteins controlling fluxes across biological membranes and represent an emerging class of drug targets. Here we searched for inhibitors of divalent metal transporters in a library of 1,676 commercially available 3D-shaped fragment-like molecules from the generated database GDB-17, which lists all possible organic molecules up to 17 atoms of C, N, O, S and halogen following simple criteria for chemical stability and synthetic feasibility. While screening against DMT1 (SLC11A2), an iron transporter associated with hemochromatosis and for which only very few inhibitors are known, only yielded two weak inhibitors, our approach led to the discovery of the first inhibitor of ZIP8 (SLC39A8), a zinc transporter associated with manganese homeostasis and osteoarthritis but with no previously reported pharmacology, demonstrating that this target is druggable.

A direct-to-biology high-throughput chemistry approach to reactive fragment screening

Methods for rapid identification of chemical tools are essential for the validation of emerging targets and to provide medicinal chemistry starting points for the development of new medicines. Here, we report a screening platform that combines 'direct-to-biology' high-throughput chemistry (D2B-HTC) with photoreactive fragments. The platform enabled the rapid synthesis of >1000 PhotoAffinity Bits (HTC-PhABits) in 384-well plates in 24 h and their subsequent screening as crude reaction products with a protein target without purification. Screening the HTC-PhABit library with carbonic anhydrase I (CAI) afforded 7 hits (0.7% hit rate), which were found to covalently crosslink in the Zn2+ binding pocket. A powerful advantage of the D2B-HTC screening platform is the ability to rapidly perform iterative design-make-test cycles, accelerating the development and optimisation of chemical tools and medicinal chemistry starting points with little investment of resource.

Advanced Computational Methodologies Used in the Discovery of New Natural Anticancer Compounds

Natural chemical compounds have been widely investigated for their programmed necrosis causing characteristics. One of the conventional methods for screening such compounds is the use of concentrated plant extracts without isolation of active moieties for understanding pharmacological activity. For the last two decades, modern medicine has relied mainly on the isolation and purification of one or two complicated active and isomeric compounds. The idea of multi-target drugs has advanced rapidly and impressively from an innovative model when first proposed in the early 2000s to one of the popular trends for drug development in 2021. Alternatively, fragment-based drug discovery is also explored in identifying target-based drug discovery for potent natural anticancer agents which is based on well-defined fragments opposite to use of naturally occurring mixtures. This review summarizes the current key advancements in natural anticancer compounds; computer-assisted/fragment-based structural elucidation and a multi-target approach for the exploration of natural compounds.

Identification of isoform/domain-selective fragments from the selection of DNA-encoded dynamic library

DNA-encoded chemical library (DEL) has emerged to be a powerful ligand screening technology in drug discovery. Recently, we reported a DNA-encoded dynamic library (DEDL) approach that combines the principle of traditional dynamic combinatorial library (DCL) with DEL. DEDL has shown excellent potential in fragment-based ligand discovery with a variety of protein targets. Here, we further tested the utility of DEDL in identifying low molecular weight fragments that are selective for different isoforms or domains of the same protein family. A 10,000-member DEDL was selected against sirtuin-1, 2, and 5 (SIRT1, 2, 5) and the BD1 and BD2 domains of bromodomain 4 (BRD4), respectively. Albeit with modest potency, a series of isoform/domain-selective fragments were identified and the corresponding inhibitors were derived by fragment linking.

Strategies for targeting undruggable targets

INTRODUCTION

Undruggable targets refer to clinically meaningful therapeutic targets that are 'difficult to drug' or 'yet to be drugged' via traditional approaches. Featuring characteristics of lacking defined ligand-binding pockets, non-catalytic protein-protein interaction functional modes and less-investigated 3D structures, these undruggable targets have been targeted with novel therapeutic entities developed with the progress of unconventional drug discovery approaches, such as targeted degradation molecules and display technologies.

AREA COVERED

This review first presents the concept of 'undruggable' exemplified by RAS and other targets. Next, detailed strategies are illustrated in two aspects: innovation of therapeutic entities and development of unconventional drug discovery technologies. Finally, case studies covering typical undruggable targets (Bcl-2, p53, and RAS) are depicted to further demonstrate the feasibility of the strategies and entities above.

EXPERT OPINION

Targeting the undruggable expands the scope of therapeutically reachable targets. Consequently, it represents the drug discovery frontier. Biomedical studies are capable of dissecting disease mechanisms, thus broadening the list of undruggable targets. Encouraged by the recent approval of the KRAS inhibitor Sotorasib, we believe that merging multiple discovery approaches and exploiting various novel therapeutic entities would pave the way for dealing with more 'undruggable' targets in the future.

Chemical Chaperones Modulate the Formation of Metabolite Assemblies

The formation of amyloid-like structures by metabolites is associated with several inborn errors of metabolism (IEMs). These structures display most of the biological, chemical and physical properties of protein amyloids. However, the molecular interactions underlying the assembly remain elusive, and so far, no modulating therapeutic agents are available for clinical use. Chemical chaperones are known to inhibit protein and peptide amyloid formation and stabilize misfolded enzymes. Here, we provide an in-depth characterization of the inhibitory effect of osmolytes and hydrophobic chemical chaperones on metabolite assemblies, thus extending their functional repertoire. We applied a combined in vivo-in vitro-in silico approach and show their ability to inhibit metabolite amyloid-induced toxicity and reduce cellular amyloid content in yeast. We further used various biophysical techniques demonstrating direct inhibition of adenine self-assembly and alteration of fibril morphology by chemical chaperones. Using a scaffold-based approach, we analyzed the physiochemical properties of various dimethyl sulfoxide derivatives and their role in inhibiting metabolite self-assembly. Lastly, we employed whole-atom molecular dynamics simulations to elucidate the role of hydrogen bonds in osmolyte inhibition. Our results imply a dual mode of action of chemical chaperones as IEMs therapeutics, that could be implemented in the rational design of novel lead-like molecules.

Aminoesterenamide Achieved by Three-Component Reaction Heading toward Tailoring Covalent Adaptable Network with Great Freedom

Covalent adaptable networks (CANs) have recently received extensive interests due to their reprocessability and repairability. Rethinking the libraries of the published CANs, most of them are fabricated by one/two-component reactions and few cases utilize multi-component reactions to construct CANs while multi-component reactions are conductive to tailoring the properties of polymers due to their structural designability and flexible choice of raw materials. A novel kind of dynamic covalent bond named aminoesterenamide is presented through three-component reaction between acetoacetyl, amine and isocyanate. Aminoesterenamide exhibits thermal reversibility through dissociating into vinylogous urethane and isocyanate. When it is used to prepare CANs, the synthesized polymer networks can be reprocessed many times via the exchange reaction between aminoesterenamides. Moreover, the forming of aminoesterenamide involving three starting components imparts CANs with great freedom to tailor their properties. Therefore, the authors believe this method that utilizes three-component reaction to fabricate CANs would bring new stories and perspectives to the exploration of new types of CANs.

The Growing Importance of Chirality in 3D Chemical Space Exploration and Modern Drug Discovery Approaches for Hit-ID: Topical Innovations

Modern-day drug discovery is now blessed with a wide range of high-throughput hit identification (hit-ID) strategies that have been successfully validated in recent years, with particular success coming from high-throughput screening, fragment-based lead discovery, and DNA-encoded library screening. As screening efficiency and throughput increases, this enables the viable exploration of increasingly complex three-dimensional (3D) chemical structure space, with a realistic chance of identifying highly specific hit ligands with increased target specificity and reduced attrition rates in preclinical and clinical development. This minireview will explore the impact of an improved design of multifunctionalized, sp3-rich, stereodefined scaffolds on the (virtual) exploration of 3D chemical space and the specific requirements for different hit-ID technologies.

Unmasking of crucial structural fragments for coronavirus protease inhibitors and its implications in COVID-19 drug discovery

Fragment based drug discovery (FBDD) by the aid of different modelling techniques have been emerged as a key drug discovery tool in the area of pharmaceutical science and technology. The merits of employing these methods, in place of other conventional molecular modelling techniques, endorsed clear detection of the possible structural fragments present in diverse set of investigated compounds and can create alternate possibilities of lead optimization in drug discovery. In this work, two fragment identification tools namely SARpy and Laplacian-corrected Bayesian analysis were used for previous SARS-CoV PLpro and 3CLpro inhibitors. A robust and predictive SARpy based fragments identification was performed which have been validated further by Laplacian-corrected Bayesian model. These comprehensive approaches have advantages since fragments are straight forward to interpret. Moreover, distinguishing the key molecular features (with respect to ECFP_6 fingerprint) revealed good or bad influences for the SARS-CoV protease inhibitory activities. Furthermore, the identified fragments could be implemented in the medicinal chemistry endeavors of COVID-19 drug discovery.

A Call to Action for New Global Approaches to Cardiovascular Disease Drug Solutions

While we continue to wrestle with the immense challenge of implementing equitable access to established evidence-based treatments, substantial gaps remain in our pharmacotherapy armament for common forms of cardiovascular disease including coronary and peripheral arterial disease, heart failure, hypertension, and arrhythmia. We need to continue to invest in the development of new approaches for the discovery, rigorous assessment, and implementation of new therapies. Currently, the time and cost to progress from lead compound/product identification to the clinic, and the success rate in getting there reduces the incentive for industry to invest, despite the enormous burden of disease and potential size of market. There are tremendous opportunities with improved phenotyping of patients currently batched together in syndromic "buckets." Use of advanced imaging and molecular markers may allow stratification of patients in a manner more aligned to biological mechanisms that can, in turn, be targeted by specific approaches developed using high-throughput molecular technologies. Unbiased "omic" approaches enhance the possibility of discovering completely new mechanisms in such groups. Furthermore, advances in drug discovery platforms, and models to study efficacy and toxicity more relevant to the human disease, are valuable. Re-imagining the relationships among discovery, translation, evaluation, and implementation will help reverse the trend away from investment in the cardiovascular space, establishing innovative platforms and approaches across the full spectrum of therapeutic development.

Efficiency of Homology Modeling Assisted Molecular Docking in G-protein Coupled Receptors

BACKGROUND

Molecular docking is in regular practice to assess ligand affinity on a target protein crystal structure. In the absence of protein crystal structure, the homology modeling or comparative modeling is the best alternative to elucidate the relationship details between a ligand and protein at the molecular level. The development of accurate homology modeling (HM) and its integration with molecular docking (MD) is essential for successful, rational drug discovery.

OBJECTIVE

The G-protein coupled receptors (GPCRs) are attractive therapeutic targets due to their immense role in human pharmacology. The GPCRs are membrane-bound proteins with the complex constitution, and the understanding of their activation and inactivation mechanisms is quite challenging. Over the past decade, there has been a rapid expansion in the number of solved G-protein-coupled receptor (GPCR) crystal structures; however, the majority of the GPCR structures remain unsolved. In this context, HM guided MD has been widely used for structure-based drug design (SBDD) of GPCRs.

METHODS

The focus of this review is on the recent (i) developments on HM supported GPCR drug discovery in the absence of GPCR crystal structures and (ii) application of HM in understanding the ligand interactions at the binding site, virtual screening, determining receptor subtype selectivity and receptor behaviour in comparison with GPCR crystal structures.

RESULTS

The HM in GPCRs has been extremely challenging due to the scarcity in template structures. In such a scenario, it is difficult to get accurate HM that can facilitate understanding of the ligand-receptor interactions. This problem has been alleviated to some extent by developing refined HM based on incorporating active /inactive ligand information and inducing protein flexibility. In some cases, HM proteins were found to outscore crystal structures.

CONCLUSION

The developments in HM have been highly operative to gain insights about the ligand interaction at the binding site and receptor functioning at the molecular level. Thus, HM guided molecular docking may be useful for rational drug discovery for the GPCRs mediated diseases.

Discovery and biological evaluation of a small-molecule inhibitor of CRM1 that suppresses the growth of triple-negative breast cancer cells

Dysregulation of the nuclear export machinery mediated by chromosomal maintenance 1 (CRM1, also known as exportin-1), is closely associated with various human disorders, such as breast cancer. Previously, we identified sulforaphene and its synthetic analogues as covalent inhibitors of CRM1. Herein, we describe the discovery and biological evaluation of another sulforaphene synthetic analogue, LFS-31, as a potential CRM1 inhibitor. In addition, we investigated the reversible binding mechanism of LFS-31 with CRM1 through molecular simulations coupled with bio-layer interferometry (BLI) and found relatively high binding affinity (KD = 43.1 ± 35.3 nM) between the LFS-31 and CRM1 groups. We found that LFS-31 exhibited a stronger growth suppression of triple-negative breast cancer (TNBC) cells than non-TNBC cells, and had minimal effect on normal breast cells. Pharmacological treatment of TNBC cells with LFS-31 at nanomolar concentrations led to the nuclear retention of IkBα resulting in strong suppression of NF-κB transcriptional activity and attenuated cell growth and proliferation, which collectively contributed to the antitumor responses. To the best of our knowledge, this is the first study to demonstrate the use of a sulforaphene analogue as a potent CRM1 inhibitor that targets the NF-κB signaling pathway for the targeted therapy of TNBC.

DMSO Solubility Assessment for Fragment-Based Screening

In this paper, we report comprehensive experimental and chemoinformatics analyses of the solubility of small organic molecules ("fragments") in dimethyl sulfoxide (DMSO) in the context of their ability to be tested in screening experiments. Here, DMSO solubility of 939 fragments has been measured experimentally using an NMR technique. A Support Vector Classification model was built on the obtained data using the ISIDA fragment descriptors. The analysis revealed 34 outliers: experimental issues were retrospectively identified for 28 of them. The updated model performs well in 5-fold cross-validation (balanced accuracy = 0.78). The datasets are available on the Zenodo platform (DOI:10.5281/zenodo.4767511) and the model is available on the website of the Laboratory of Chemoinformatics.

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.

Asymmetric organocatalysis: an enabling technology for medicinal chemistry

The efficacy and synthetic versatility of asymmetric organocatalysis have contributed enormously to the field of organic synthesis since the early 2000s. As asymmetric organocatalytic methods mature, they have extended beyond the academia and undergone scale-up for the production of chiral drugs, natural products, and enantiomerically enriched bioactive molecules. This review provides a comprehensive overview of the applications of asymmetric organocatalysis in medicinal chemistry. A general picture of asymmetric organocatalytic strategies in medicinal chemistry is firstly presented, and the specific applications of these strategies in pharmaceutical synthesis are systematically described, with a focus on the preparation of antiviral, anticancer, neuroprotective, cardiovascular, antibacterial, and antiparasitic agents, as well as several miscellaneous bioactive agents. The review concludes with a discussion of the challenges, limitations and future prospects for organocatalytic asymmetric synthesis of medicinally valuable compounds.

Bench-to-Bedside in Vascular Medicine: Optimizing the Translational Pipeline for Patients With Peripheral Artery Disease

Peripheral arterial disease is a growing worldwide problem with a wide spectrum of clinical severity and is projected to consume >$21 billion per year in the United States alone. While vascular researchers have brought several therapies to the clinic in recent years, few of these approaches have leveraged advances in high-throughput discovery screens, novel translational models, or innovative trial designs. In the following review, we discuss recent advances in unbiased genomics and broader omics technology platforms, along with preclinical vascular models designed to enhance our understanding of disease pathobiology and prioritize targets for additional investigation. Furthermore, we summarize novel approaches to clinical studies in subjects with claudication and ischemic ulceration, with an emphasis on streamlining and accelerating bench-to-bedside translation. By providing a framework designed to enhance each aspect of future clinical development programs, we hope to enrich the pipeline of therapies that may prevent loss of life and limb for those with peripheral arterial disease.

Automated and enabling technologies for medicinal chemistry

Having always been driven by the need to get new treatments to patients as quickly as possible, drug discovery is a constantly evolving process. This chapter will review how medicinal chemistry was established, how it has changed over the years due to the emergence of new enabling technologies, and how early advances in synthesis, purification and analysis, have provided the foundations upon which the current automated and enabling technologies are built. Looking beyond the established technologies, this chapter will also consider technologies that are now emerging, and their impact on the future of drug discovery and the role of medicinal chemists.

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.

Design, synthesis, and molecular docking study of novel quinoline-based bis-chalcones as potential antitumor agents

A novel series of quinoline-based symmetrical and unsymmetrical bis-chalcones was synthesized via a Claisen-Schmidt condensation reaction between 3-formyl-quinoline/quinolone derivatives with acetone or arylidene acetones, respectively, by using KOH/MeOH/H₂ O as a reaction medium. Twelve of the obtained compounds were evaluated for their in vitro cytotoxic activity against 60 different human cancer cell lines according to the National Cancer Institute protocol. Among the screened compounds, the symmetrical N-butyl bis-quinolinyl-chalcone 14g and the unsymmetrical quinolinyl-bis-chalcone 17o bearing a 7-chloro-substitution on the N-benzylquinoline moiety and 4-hydroxy-3-methoxy substituent on the phenyl ring, respectively, exhibited the highest overall cytotoxicity against the evaluated cell lines with a GI₅₀ range of 0.16-5.45 µM, with HCT-116 (GI₅₀  = 0.16) and HT29 (GI₅₀  = 0.42 μM) (colon cancer) representing best-case scenarios. Notably, several GI₅₀ values for these compounds were lower than those of the reference drugs doxorubicin and 5-FU. Docking studies performed on selected derivatives yielded very good binding energies in the active site of proteins that participate in key carcinogenic pathways.

Simultaneous Identification of Multiple Binding Sites in Proteins: A Statistical Mechanics Approach

We present an extension of the Poisson-Boltzmann model in which the solute of interest is immersed in an assembly of self-orienting Langevin water dipoles, anions, cations, and hydrophobic molecules, all of variable densities. Interactions between charges are controlled by electrostatics, while hydrophobic interactions are modeled with a Yukawa potential. We impose steric constraints by assuming that the system is represented on a cubic lattice. We also assume incompressibility; i.e., all sites of the lattice are occupied. This model, which we refer to as the Hydrophobic Dipolar Poisson-Boltzmann Langevin (HDPBL) model, leads to a system of two equations whose solutions give the water dipole, salt, and hydrophobic molecule densities, all of them in the presence of the others in a self-consistent way. We use those to study the organization of the ions, cosolvent, and solvent molecules around proteins. In particular, peaks of densities are expected to reveal, simultaneously, the presence of compatible binding sites of different kinds on a protein. We have tested and validated the ability of HDPBL to detect pockets in proteins that bind to hydrophobic ligands, polar ligands, and charged small probes as well as to characterize the binding sites of lipids for membrane proteins.

19F-Tagged metal binding pharmacophores for NMR screening of metalloenzymes

This study demonstrates the screening of a collection of twelve 19F-tagged metal-binding pharmacophores (MBPs) against the Zn(ii)-dependent metalloenzyme human carbonic anhydrase II (hCAII) by 19F NMR. The isomorphous replacement of Zn(ii) by Co(ii) in hCAII produces enhanced sensitivity and reveals the potential of 19F NMR-based techniques for metalloenzyme ligand discovery.

Biomolecular Simulations with the Three-Dimensional Reference Interaction Site Model with the Kovalenko-Hirata Closure Molecular Solvation Theory

The statistical mechanics-based 3-dimensional reference interaction site model with the Kovalenko-Hirata closure (3D-RISM-KH) molecular solvation theory has proven to be an essential part of a multiscale modeling framework, covering a vast region of molecular simulation techniques. The successful application ranges from the small molecule solvation energy to the bulk phase behavior of polymers, macromolecules, etc. The 3D-RISM-KH successfully predicts and explains the molecular mechanisms of self-assembly and aggregation of proteins and peptides related to neurodegeneration, protein-ligand binding, and structure-function related solvation properties. Upon coupling the 3D-RISM-KH theory with a novel multiple time-step molecular dynamic (MD) of the solute biomolecule stabilized by the optimized isokinetic Nosé-Hoover chain thermostat driven by effective solvation forces obtained from 3D-RISM-KH and extrapolated forward by generalized solvation force extrapolation (GSFE), gigantic outer time-steps up to picoseconds to accurately calculate equilibrium properties were obtained in this new quasidynamics protocol. The multiscale OIN/GSFE/3D-RISM-KH algorithm was implemented in the Amber package and well documented for fully flexible model of alanine dipeptide, miniprotein 1L2Y, and protein G in aqueous solution, with a solvent sampling rate ~150 times faster than a standard MD simulation in explicit water. Further acceleration in computation can be achieved by modifying the extent of solvation layers considered in the calculation, as well as by modifying existing closure relations. This enhanced simulation technique has proven applications in protein-ligand binding energy calculations, ligand/solvent binding site prediction, molecular solvation energy calculations, etc. Applications of the RISM-KH theory in molecular simulation are discussed in this work.

Inspecting the Mechanism of Fragment Hits Binding on SARS-CoV-2 Mpro by Using Supervised Molecular Dynamics (SuMD) Simulations

Computational approaches supporting the early characterization of fragment molecular recognition mechanism represent a valuable complement to more expansive and low‐throughput experimental techniques. In this retrospective study, we have investigated the geometric accuracy with which high‐throughput supervised molecular dynamics simulations (HT‐SuMD) can anticipate the experimental bound state for a set of 23 fragments targeting the SARS‐CoV‐2 main protease. Despite the encouraging results herein reported, in line with those previously described for other MD‐based posing approaches, a high number of incorrect binding modes still complicate HT‐SuMD routine application. To overcome this limitation, fragment pose stability has been investigated and integrated as part of our in‐silico pipeline, allowing us to prioritize only the more reliable predictions.

Reimagining high-throughput profiling of reactive cysteines for cell-based screening of large electrophile libraries

Current methods used for measuring amino acid side-chain reactivity lack the throughput needed to screen large chemical libraries for interactions across the proteome. Here we redesigned the workflow for activity-based protein profiling of reactive cysteine residues by using a smaller desthiobiotin-based probe, sample multiplexing, reduced protein starting amounts and software to boost data acquisition in real time on the mass spectrometer. Our method, streamlined cysteine activity-based protein profiling (SLC-ABPP), achieved a 42-fold improvement in sample throughput, corresponding to profiling library members at a depth of >8,000 reactive cysteine sites at 18 min per compound. We applied it to identify proteome-wide targets of covalent inhibitors to mutant Kirsten rat sarcoma (KRAS)^G12C and Bruton's tyrosine kinase (BTK). In addition, we created a resource of cysteine reactivity to 285 electrophiles in three human cell lines, which includes >20,000 cysteines from >6,000 proteins per line. The goal of proteome-wide profiling of cysteine reactivity across thousand-member libraries under several cellular contexts is now within reach.

Genetically Encoded Fragment-Based Discovery from Phage-Displayed Macrocyclic Libraries with Genetically Encoded Unnatural Pharmacophores

Genetically encoded macrocyclic peptide libraries with unnatural pharmacophores are valuable sources for the discovery of ligands for many targets of interest. Traditionally, generation of such libraries employs "early stage" incorporation of unnatural building blocks into the chemically or translationally produced macrocycles. Here, we describe a divergent late-stage approach to such libraries starting from readily available starting material: genetically encoded libraries of peptides. A diketone linchpin 1,5-dichloropentane-2,4-dione converts peptide libraries displayed on phage to 1,3-diketone bearing macrocyclic peptides (DKMP): shelf-stable precursors for Knorr pyrazole synthesis. Ligation of diverse hydrazine derivatives onto DKMP libraries displayed on phage that carries silent DNA-barcodes yields macrocyclic libraries in which the amino acid sequence and the pharmacophore are encoded by DNA. Selection of this library against carbonic anhydrase enriched macrocycles with benzenesulfonamide pharmacophore and nanomolar Kd. The methodology described in this manuscript can graft diverse pharmacophores into many existing genetically encoded phage libraries and significantly increase the value of such libraries in molecular discoveries.