Fragment based drug design is like a chess game in that a good or a bad move can dramatically influence the outcome. In the design process, it is important to identify the key binding site residues (hotspots) that can have a substantial impact on ligand potency and efficiency. Here, we introduce FMOPhore algorithm represented with a scoring function named FP-score, which combines Quantum Mechanics Fragment Molecular Orbital calculations with 3D-protein-ligand pharmacophore models. FP-score accurately classifies binding site residues in two classes: 1) Hotspot residues (Delineated into three categories; Anchor, Transient, and Accessible) and 2) non-hotspot residues. We apply our algorithm in two different scenarios: holo-complex and apo-structure, testing its robustness on 46 different protein targets including an experimental case study on drug-resistance hotspots across 829 protein-ligand complexes. We handle protein binding site flexibility using Dy-FMOPhore which improves the detection of hotspots. FMOPhore provides valuable insight for efficient, selective fragment growing and lead optimization strategies.

In this article, we detail our latest findings toward developing a diversified series of potential nonhormonal contraceptive compounds using a phenotypic screening approach against human sperm. Phenotypic screening of nine compound libraries (88,773 compounds in total) was conducted using an in-house automated robotic screening platform, allowing quantification of sperm motility in samples pretreated with the compounds. From these screens, 9 chemical series were identified and investigated in hit expansion programs, with a particular focus on identifying chemical matter that selectively reduces sperm motility without any significant cytotoxicity in somatic cells (HepG2 cells). While there were no clinically progressable leads identified, the study did identify some useful tool compounds for research into the fundamental biology underpinning nonhormonal contraceptive discovery, and a lot was learned about the screening technology, which sets us up for future screening to identify and develop better chemical starting points.

Identification of novel drug targets is a key component of modern drug discovery. While antimalarial targets are often identified through the mechanism of action studies on phenotypically derived inhibitors, this method tends to be time- and resource-consuming. The discoverable target space is also constrained by existing compound libraries and phenotypic assay conditions. Leveraging recent advances in protein structure prediction, we systematically assessed the Plasmodium falciparum genome and identified 867 candidate protein targets with evidence of small-molecule binding and blood-stage essentiality. Of these, 540 proteins showed strong essentiality evidence and lack inhibitors that have progressed to clinical trials. Expert review and rubric-based scoring of this subset based on additional criteria such as selectivity, structural information, and assay developability yielded 27 high-priority antimalarial target candidates. This study also provides a genome-wide data resource for P. falciparum and implements a generalizable framework for systematically evaluating and prioritizing novel pathogenic disease targets.

False-positives plague High Throughput Screening in general and are costly as they consume resource and time to resolve. Methods that can rapidly identify such compounds at the initial screen are therefore of great value. Advances in mass spectrometry have led to the ability to screen inhibitors in drug discovery applications by direct detection of an enzyme reaction product. The technique is free from some of the artefacts that trouble classical assays such as fluorescence interference. Its direct nature negates the need for coupling enzymes and hence is simpler with fewer opportunities for artefacts. Despite its myriad advantages, we report here a mechanism for false-positive hits which has not been reported in the literature. Further we have developed a pipeline for detecting these false-positive hits and suggest a method to mitigate against them. ### Competing Interest Statement The authors have declared no competing interest.

The lack of novel drug targets is a barrier to the development of therapeutics for infectious diseases. In this paper the design and construction of a diverse set of small molecule fragments attached to functionalised linkers is discussed. The functionalised linkers are comprised of a diazirine photo crosslinking moiety alongside an acetylene group to facilitate target pulldown. These are designed to be probes to find new targets, though phenotypic screening, followed by cross coupling and pull-down of the target. They also have the potential to be used in screening programmes against whole cells, enzymes and receptors. To help reduce the number of false positives, a set of the fragments with an un-reactive acetamide has also been prepared, which will allow competition studies.

False-positives plague high-throughput screening in general and are costly as they consume resource and time to resolve. Methods that can rapidly identify such compounds at the initial screen are therefore of great value. Advances in mass spectrometry have led to the ability to screen inhibitors in drug discovery applications by direct detection of an enzyme reaction product. The technique is free from some of the artefacts that trouble classical assays such as fluorescence interference. Its direct nature negates the need for coupling enzymes and hence is simpler with fewer opportunities for artefacts. Despite its myriad advantages, we report here a mechanism for false-positive hits which has not been reported in the literature. Further we have developed a pipeline for detecting these false-positive hits and suggest a method to mitigate against them.

Herein, we demonstrate the use of a commercially available enzymatic kit to achieve late-stage hydroxylation of biologically relevant compounds by using the PolyCYPs screening kit. A selection of promising biotransformations were scaled up, products isolated, and structures elucidated. Isolated compounds were screened against a range of pathogens, namely, Schistosoma mansoni, Leishmania donovani, Trypanosoma cruzi, and Trypanosoma brucei to obtain biological data. This approach has allowed data generation more efficiently than the chemical synthesis of the same molecules. Importantly, it has been demonstrated that production of hits of interest can also be scaled up to enable further study. We also demonstrate the biosynthetic synthesis of a lead compound in fewer steps than using standard synthetic chemistry, offering faster access to compounds for screening or further transformation. This approach has the potential to save time and resources in a drug discovery program, by reducing the necessity to synthesize late-stage intermediates and develop new chemistry.

Combining antibiotics remains a promising approach to treat patients and to prevent the emergence of antimicrobial resistance

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mt), is one of the deadliest infectious diseases. The rise of multidrug-resistant strains represents a major public health threat, requiring new therapeutic options. Bacterial aminoacyl-tRNA synthetases (aaRS) have been shown to be highly promising drug targets, including for TB treatment. These enzymes play an essential role in translating the DNA gene code into protein sequence by attaching specific amino acid to their cognate tRNAs. They have multiple binding sites that can be targeted for inhibitor discovery: amino acid binding pocket, ATP binding pocket, tRNA binding site and an editing domain. Recently we reported several high-resolution structures of M. tuberculosis phenylalanyl-tRNA synthetase (MtPheRS) complexed with tRNAPhe and either L-Phe or a nonhydrolyzable phenylalanine adenylate analog. Here, using Nucleic Magnetic Resonance (NMR) and Surface Plasmon Resonance (SPR) we identified fragments that bind to MtPheRS and we determined crystal structures of their complexes with MtPheRS/tRNAPhe. All the binders interact with the L-Phe amino acid binding site. The analysis of interactions of the new compounds combined with adenylate analog structure provides insights for the rational design of anti-tuberculosis drugs. The 3’ arm of the tRNAPhe in all the structures was disordered with exception of one complex with D-735 compound. In this structure the 3’ CCA end of the acceptor stem is observed in the editing domain of MtPheRS providing insights regarding the post-transfer editing activity of class II aaRS.

Schistosomiasis affects around 140 million people worldwide, with the highest impact on people living in low- or middle-income countries. Although the number of deaths due to schistosomiasis seems relatively low, there is a long-term health burden on patients with chronic or repeat infections. Many historical anthelmintic drugs approved for the treatment of schistosomiasis are no longer in use due to a lack of efficacy or intolerable side effects. Praziquantel remains the only available treatment, but it has some considerable disadvantages—suboptimal efficacy against juvenile worms and a potential risk of developing resistance. Despite an obvious need for alternative antischistosomals, no new treatment options have emerged in the last 40 years and there are very few drug discovery programs working in this area. To tackle schistosomiasis, there is a need to establish a community-agreed target product profile to outline desirable criteria for developing new treatments. Recognizing the clinical impact of schistosomiasis and identifying the key requirements will help inform drug discovery efforts.

Antibiotic resistance is among the greatest threats of the modern era. Multidrug efflux pumps expel antibiotics from bacterial cells and present a particular challenge by conferring resistance to a broad range of antibiotic classes; however, there is currently a lack of potent and selective inhibitors. Here, we report the discovery of IMP-2380 , a drug-like chemical probe for the multidrug efflux pump NorA that delivers low-nanomolar potentiation of ciprofloxacin activity in vitro and activity in an in vivo S. aureus infection model. A phenotypic high-throughput screen for inhibitors of the ciprofloxacin-activated SOS DNA repair pathway in methicillin-resistant Staphylococcus aureus (MRSA) identified hit compounds targeting NorA, and subsequent optimization established IMP-2380 as the most potent NorA inhibitor discovered to date. The structure of NorA bound to IMP-2380 was solved by cryo-electron microscopy at 2.52 Å resolution, revealing that the small molecule locks the pump in the 'outward-open' conformation. This closes the inner face and prevents antibiotics binding from the cytosol, providing an explanation for the exceptional potency of IMP-2380 and structure-activity relationship across the series. IMP-2380 represents an in vivo active NorA inhibitor, functioning via a structurally defined outward-open binding mode, and will enable future exploration of NorA as a druggable target to combat antibiotic resistance.

Acoustic droplet ejection (ADE) has been utilised to miniaturise reagent reactivity screening. By dispensing nanolitre volumes of an SNAr reagent library and reacting with morpholine an understanding of synthetic reactivity profiles was obtained before committing significant amounts of material towards synthetic array synthesis.

The identification of novel drug targets for the purpose of designing small molecule inhibitors is key component to modern drug discovery. In malaria parasites, discoveries of antimalarial targets have primarily occurred retroactively by investigating the mode of action of compounds found through phenotypic screens. Although this method has yielded many promising candidates, it is time- and resource-consuming and misses targets not captured by existing antimalarial compound libraries and phenotypic assay conditions. Leveraging recent advances in protein structure prediction and data mining, we systematically assessed the Plasmodium falciparum genome for proteins amenable to target-based drug discovery, identifying 867 candidate targets with evidence of small molecule binding and blood stage essentiality. Of these, 540 proteins showed strong essentiality evidence and lack inhibitors that have progressed to clinical trials. Expert review and rubric-based scoring of this subset based on additional criteria such as selectivity, structural information, and assay developability yielded 67 high priority candidates. This study also provides a genome-wide data resource and implements a generalizable framework for systematically evaluating and prioritizing novel pathogenic disease targets.

Cryptosporidiosis is a diarrheal disease caused by infection with Cryptosporidium spp. parasites and is a leading cause of death in malnourished children worldwide. The only approved treatment, nitazoxanide, has limited efficacy in this at-risk patient population. Additional safe therapeutics are urgently required to tackle this unmet medical need. However, the development of anti-cryptosporidial drugs is hindered by a lack of understanding of the optimal compound properties required to treat this gastrointestinal infection. To address this knowledge gap, a diverse set of potent lysyl-tRNA synthetase inhibitors was profiled to identify optimal physicochemical and pharmacokinetic properties required for efficacy in a chronic mouse model of infection. The results from this comprehensive study illustrated the importance of balancing solubility and permeability to achieve efficacy in vivo. Our results establish in vitro criteria for solubility and permeability that are predictive of compound efficacy in vivo to guide the optimization of anti-cryptosporidial drugs. Two compounds from chemically distinct series (DDD489 and DDD508) were identified as demonstrating superior efficacy and prioritized for further evaluation. Both compounds achieved marked parasite reduction in immunocompromised mouse models and a disease-relevant calf model of infection. On the basis of these promising data, these compounds have been selected for progression to preclinical safety studies, expanding the portfolio of potential treatments for this neglected infectious disease.

Protein-ligand binding prediction typically relies on docking methodologies and associated scoring functions to propose the binding mode of a ligand in a biological target. Significant challenges are associated with this approach, including the flexibility of the protein-ligand system, solvent-mediated interactions, and associated entropy changes. In addition, scoring functions are only weakly accurate due to the short time required for calculating enthalpic and entropic binding interactions. The workflow described here attempts to address these limitations by combining supervised molecular dynamics with dynamical averaging quantum mechanics fragment molecular orbital. This combination significantly increased the ability to predict the experimental binding structure of protein-ligand complexes independent from the starting position of the ligands or the binding site conformation. We found that the predictive power could be enhanced by combining the residence time and interaction energies as descriptors in a novel scoring function named the P-score. This is illustrated using six different protein-ligand targets as case studies.

Male contraceptive options and infertility treatments are limited, and almost all innovation has been limited to updates to medically assisted reproduction protocols and methods. To accelerate the development of drugs that can either improve or inhibit fertility, we established a small molecule library as a toolbox for assay development and screening campaigns using human spermatozoa. We have profiled all compounds in the Sperm Toolbox in several automated high-throughput assays that measure stimulation or inhibition of sperm motility or the acrosome reaction. We have assayed motility under non-capacitating and capacitating conditions to distinguish between pathways operating under these different physiological states. We also assayed cell viability to ensure any effects on sperm function are specific. A key advantage of our studies is that all compounds are assayed together in the same experimental conditions, which allows quantitative comparisons of their effects in complementary functional assays. We have combined the resulting datasets to generate fingerprints of the Sperm Toolbox compounds on sperm function. The data are included in an on-line R-based app for convenient querying.

The optimization of compounds' binding affinity for a biological target is a crucial aspect of the drug development process. Being able to accurately predict binding energies in advance of synthesizing compounds would have a massive impact on the speed of the drug discovery process. The ideal binding affinity prediction method should combine accuracy, reliability, and speed. In this paper, we present SophosQM, a quantum mechanics (QM)-based approach, which can accurately predict the binding affinities of compounds to proteins. The binding affinity predictive models generated by SophosQM are based on the fragment molecular orbital (FMO) method to estimate the enthalpic component of the binding free energy, and a macroscopic descriptor, clog P, is used as an approximation of the entropic component. The affinity prediction is performed using multilinear regression, fitting the experimental values against the FMO-computed enthalpic term and clog P. The quality of the prediction can be assessed in terms of the correlation coefficient between experimental and predicted values. In this work, the method's reliability and accuracy are exemplified by applying SophosQM to 70 compounds binding to six different targets of pharmaceutical relevance. Overall, the results show a very satisfactory performance with a global correlation coefficient in the order of 0.9. Our predictions also show a satisfactory performance compared to data based on free energy perturbation. Finally, SophosQM can also be applied in high-throughput mode by using semiempirical QM methods to evaluate large portions of chemical space, while retaining a good level of accuracy, but decreasing the computing time to just a few seconds per compound.

There is an urgent need for the development of new therapeutics with novel modes of action to target Gram-negative bacterial infections, due to resistance to current drugs. Previously, FabA, an enzyme in the bacterial type II fatty acid biosynthesis pathway, was identified as a potential drug target in Pseudomonas aeruginosa, a Gram-negative bacteria of significant clinical concern. A chemical starting point was also identified. There is a cysteine, Cys15, in the active site of FabA, adjacent to where this compound binds. This paper describes the preparation of analogues containing an electrophilic warhead with the aim of covalent inhibition of the target. A wide variety of analogues were successfully prepared. Unfortunately, these analogues did not increase inhibition, which may be due to a loop within the enzyme partially occluding access to the cysteine.

Protein ligand binding prediction typically relies on docking methodologies and associated scoring functions to propose the binding mode of a ligand in a biological target. Significant challenges are associated with this approach, including the flexibility of the protein-ligand system, solvent-mediated interactions and associated entropy changes. In addition, scoring functions are only weakly accurate due to the short time required for calculating enthalpic and entropic binding interactions. The workflow described here attempts to address these limitations by combining Supervised Molecular Dynamics (SuMD) with Dynamical Averaging Quantum Mechanics Fragment Molecular Orbital (DA-QM-FMO). This is illustrated using a set of five ligands targeting the SARS-CoV-2 Papain-like protease protein. This combination significantly increased the ability to predict the experimental binding structure of protein-ligand complexes independent from the starting position of the ligands or the binding site conformation. We found that the predictive power could be enhanced by combining the residence time (SuMD) and interaction energies (DA-QM-FMO) as descriptors in a novel scoring function named the P-score.

AbstractMale contraceptive options and infertility treatments are limited. To help overcome these limitations we established a small molecule library as a toolbox for assay development and screening campaigns using human spermatozoa. We have profiled all compounds in our automated high-throughput screening platform and provide the dataset, which allows direct side-by-side comparisons of compound effects and assays using live human spermatozoa.