Efforts to tackle malaria must continue for a disease that threatens half of the global population. Parasite resistance to current therapies requires new chemotypes that are able to demonstrate effectiveness and safety. Previously, we developed a machine-learning-based approach to predict compound antimalarial activity, which was trained on the compound collections of several organizations. The resulting prediction platform, MAIP, was made freely available to the scientific community and offers a solution to prioritize molecules of interest in virtual screening and hit-to-lead optimization. Here, we experimentally validate MAIP and demonstrate how the approach was used in combination with a robust compound selection workflow and a recently introduced innovative high-throughput screening (HTS) cascade to select and purchase compounds from a public library for subsequent experimental screening. We observed a 12-fold enrichment compared with a randomly selected set of molecules, and the eight hits we ultimately selected exhibit good potency and absorption, distribution, metabolism, and excretion (ADME) profiles.

A central challenge of antimalarial therapy is the emergence of resistance to the components of artemisinin-based combination therapies (ACTs) and the urgent need for new drugs acting through novel mechanism of action. Over the last decade, compounds identified in phenotypic high throughput screens (HTS) have provided the starting point for six candidate drugs currently in the Medicines for Malaria Venture (MMV) clinical development portfolio. However, the published screening data which provided much of the new chemical matter for malaria drug discovery projects have been extensively mined. Here we present a new screening and selection cascade for generation of hit compounds active against the blood stage of Plasmodium falciparum. In addition, we validate our approach by testing a library of 141,786 compounds not reported earlier as being tested against malaria. The Hit Generation Library 1 (HGL1) was designed to maximise the chemical diversity and novelty of compounds with physicochemical properties associated with potential for further development. A robust HTS cascade containing orthogonal efficacy and cytotoxicity assays, including a newly developed and validated nanoluciferase-based assay was used to profile the compounds. 75 compounds (Screening Active hit rate of 0.05%) were identified meeting our stringent selection criteria of potency in drug sensitive (NF54) and drug resistant (Dd2) parasite strains (IC50 ≤ 2 µM), rapid speed of action and cell viability in HepG2 cells (IC50 ≥ 10 µM). Following further profiling, 33 compounds were identified that meet the MMV Confirmed Active profile and are high quality starting points for new antimalarial drug discovery projects.

The current Covid-19 pandemic has underlined the need for amore coordinated and forward-looking investment in the search for newmedicines targeting emerging health care threats. Repositioning currentlyapproved drugs is a popular approach to any new emerging disease, but itrepresents afirst wave of response. Behind this would be a second wave of morespecifically designed therapies based on activities against specific moleculartargets or in phenotypic assays. Following the successful deployment and uptakeof previous open access compound collections, we assembled the PandemicResponse Box, a collection of 400 compounds to facilitate drug discovery inemerging infectious disease. These are based on public domain information onchemotypes currently in discovery and early development which have beenshown to have useful activities and were prioritized by medicinal chemistryexperts. They are freely available to the community as a pharmacological test setwith the understanding that data will be shared rapidly in the public domain.

The hormone adrenomedullin has both physiological and pathological roles in biology. As a potent vasodilator, adrenomedullin is critically important in the regulation of blood pressure, but it also has several roles in disease, of which its actions in cancer are becoming recognized to have clinical importance. Reduced circulating adrenomedullin causes increased blood pressure but also reduces tumor progression, so drugs blocking all effects of adrenomedullin would be unacceptable clinically. However, there are two distinct receptors for adrenomedullin, each comprising the same G protein-coupled receptor (GPCR), the calcitonin receptor-like receptor (CLR), together with a different accessory protein known as a receptor activity-modifying protein (RAMP). The CLR with RAMP2 forms an adrenomedullin-1 receptor, and the CLR with RAMP3 forms an adrenomedullin-2 receptor. Recent research suggests that a selective blockade of adrenomedullin-2 receptors would be therapeutically valuable. Here we describe the design, synthesis, and characterization of potent small-molecule adrenomedullin-2 receptor antagonists with 1000-fold selectivity over the adrenomedullin-1 receptor, although retaining activity against the CGRP receptor. These molecules have clear effects on markers of pancreatic cancer progression in vitro, drug-like pharmacokinetic properties, and inhibit xenograft tumor growth and extend life in a mouse model of pancreatic cancer. Taken together, our data support the promise of a new class of anticancer therapeutics as well as improved understanding of the pharmacology of the adrenomedullin receptors and other GPCR/RAMP heteromers.

Convolutional neural networks (CNN) have been shown to provide a good solution for classification problems that utilize data obtained from vibrational spectroscopy. Moreover, CNNs are capable of identifying substances from noisy spectra without the need for additional preprocessing. However, their application in practical spectroscopy is restricted due to two reasons. First the effectiveness of classification using CNNs diminishes rapidly when only a small number of spectra per substance are available for training (which is a typical situation in real applications). Secondly, to accommodate new, previously unseen, substance classes the network must be retrained which is computationally intensive. Here we address these issues by reformulating a multi-class classification problem with a large number of classes to a binary classification problem for which the available data is sufficient for representation learning. Hence, we define the learning task as identifying pairs of inputs as belonging to the same class or different classes. We achieve this using a Siamese convolutional neural network. A novel sampling strategy is proposed to address the imbalance problem in training the Siamese network. The trained network can classify samples of previously unseen substance classes using just a single reference sample (termed as one-shot learning in the machine learning community). Our results on three independent Raman datasets demonstrate much better accuracy than other practical systems to date, while allowing effortless updates of the system's database with new substance classes.

Type I protein arginine methyltransferases (PRMTs) catalyze asymmetric dimethylation of arginines on proteins. Type I PRMTs and their substrates have been implicated in human cancers, suggesting inhibition of type I PRMTs may offer a therapeutic approach for oncology. The current report describes GSK3368715 (EPZ019997), a potent, reversible type I PRMT inhibitor with anti-tumor effects in human cancer models. Inhibition of PRMT5, the predominant type II PRMT, produces synergistic cancer cell growth inhibition when combined with GSK3368715. Interestingly, deletion of the methylthioadenosine phosphorylase gene (MTAP) results in accumulation of the metabolite 2-methylthioadenosine, an endogenous inhibitor of PRMT5, and correlates with sensitivity to GSK3368715 in cell lines. These data provide rationale to explore MTAP status as a biomarker strategy for patient selection.

Visceral leishmaniasis is a severe parasitic disease that is one of the most neglected tropical diseases. Treatment options are limited, and there is an urgent need for new therapeutic agents. Following an HTS campaign and hit optimization, a novel series of amino-pyrazole ureas has been identified with potent in vitro antileishmanial activity. Furthermore, compound 26 shows high levels of in vivo efficacy (>90%) against Leishmania infantum, thus demonstrating proof of concept for this series.

Drug discovery faces economic and scientific imperatives to deliver lead molecules rapidly and efficiently. Using traditional paradigms the molecular design, synthesis, and screening loops enforce a significant time delay leading to inefficient use of data in the iterative molecular design process. Here, we report the application of a flow technology platform integrating the key elements of structure-activity relationship (SAR) generation to the discovery of novel Abl kinase inhibitors. The platform utilizes flow chemistry for rapid in-line synthesis, automated purification, and analysis coupled with bioassay. The combination of activity prediction using Random-Forest regression with chemical space sampling algorithms allows the construction of an activity model that refines itself after every iteration of synthesis and biological result. Within just 21 compounds, the automated process identified a novel template and hinge binding motif with pIC50 > 8 against Abl kinase--both wild type and clinically relevant mutants. Integrated microfluidic synthesis and screening coupled with machine learning design have the potential to greatly reduce the time and cost of drug discovery within the hit-to-lead and lead optimization phases.

The use of computational techniques to remove frequent hitters and enable the identification of a number of TRPV4 series from an HTS campaign will be described. The hit-to-lead efforts in one such series will also be discussed.

Given the large amounts of screening data now available, empirical methods derived from matched-molecular pairs are being used as a means for suggesting bioisosteric replacements to the medicinal chemist. The pairwise analysis of compounds has been extended to the pairwise analysis of series to bring further context to these suggestions. A validation dataset derived from recent literature has been used to demonstrate that, given a series of active compounds, this approach would be expected to predict a more potent compound, if it exists, in around 46% of cases. The approach has been successfully applied to a series of TRPA1 antagonists.

The V1a receptor has emerged as an attractive target for a range of indications including Raynaud's disease and dysmenorrhoea. As part of an effort to discover a new class of orally active V1a antagonist, we optimised a highly lipophilic, metabolically unstable lead into a range of potent, selective and metabolically stable V1a antagonists. In this communication, we demonstrate the series-dependent effect of limiting the number of rotatable bonds in order to decrease Cytochrome P450-mediated metabolism. This effort culminated in the discovery of PF-184563, a novel, selective V1a antagonist with excellent in vitro and in vivo properties.

The postprocessing of high-throughput screening (HTS) results is complicated by the occurrence of false positives (inactive compounds misidentified as active by the primary screen) and false negatives (active compounds misidentified as inactive by the primary screen). An activity cutoff is frequently used to select "active" compounds from HTS data; however, this approach is insensitive to both false positives and false negatives. An alternative method that can minimize the occurrence of these artifacts will increase the efficiency of hit selection and therefore lead discovery. In this work, rather than merely using the activity of a given compound, we look at the presence and absence of activity among all compounds in its "chemical space neighborhood" to give a degree of confidence in its activity. We demonstrate that this local hit rate (LHR) analysis method outperforms hit selection based on ranking by primary screen activity values across ten diverse high throughput screens, spanning both cell-based and biochemical assay formats of varying biology and robustness. On average, the local hit rate analysis method was approximately 2.3-fold and approximately 1.3-fold more effective in identifying active compounds and active chemical series, respectively, than selection based on primary activity alone. Moreover, when applied to finding false negatives, this method was 2.3-fold better than ranking by primary activity alone. In most cases, novel hit series were identified that would have otherwise been missed. Additional uses of and observations regarding this HTS analysis approach are also discussed.

A novel program for the superposition of flexible molecules, slate, is presented. It uses simulated annealing to minimise the difference between the distance matrices calculated from the hydrogen-bonding and aromatic-ring properties of two ligands. A method for generating a molecular stack using multiple pairwise matches is illustrated. These stacks are used by the program doh to predict the relative positions of receptor atoms that could form hydrogen bonds to two or more ligands in the dataset. The methodology has been applied to ligands binding to dihydrofolate reductase, thermolysin, H3 histamine receptors, α2 adrenoceptors and 5-HT1D receptors. When there are sufficient numbers and diversity of molecules in the dataset, the prediction of receptor-atom positions is applicable to compound design.

Feng shui is a practice that has affected the development of traditional cultural landscapes in East Asia. It arises out of Chinese concepts regarding yin-yang theory and qi energy. Western reactions to the practice of feng shui have varied. In the nineteenth century, the general response was negative, and Westerners described the practice as superstitious, irrational, and unprogressive. In the early part of the twentieth century the few accounts that dealt with the practice were less critical, but still portrayed the practice as irrational. Academic studies offeng shui since the 1960's have viewed the practice as a legitimate and sophisticated aspect oftraditional culture. Such academic works have been overshadowed by thousands ofpopular discourses on the topic which have appeared in the last ten years. Although much of the interest surrounding feng shui in the postmodern West seems based on commercial possibilities, there are some significant lessons that geographers can learn from these developments.

Hydrogen bonds are the most specific, and therefore predictable of the intermolecular interactions involved in ligand–protein binding. Given the structure of a molecule, it is possible to estimate the positions at which complementary hydrogen-bonding atoms could be found. Crystal-survey data are used in the design of a program, HBMAP, that generates a hydrogen-bond map for any given ligand, which contains all the feasible positions at which a complementary atom could be found. On superposition of ligands, the overlapping regions of their maps represent positions of receptor atoms to which each molecule can bind. The certainty of these positions is increased by the incorporation of a larger number and diversity of molecules. In this work, superposition is achieved using the program HBMATCH, which uses simulated annealing to generate the correspondence between points from the hydrogen-bonding maps of the two molecules. Equivalent matches are distinguished on the basis of their steric similarity. The strategy is tested on a number of ligands for which ligand–protein complexes have been solved crystallographically, which allows validation of the techniques. The receptor atom positions of thermolysin are successfully predicted when the correct superposition is obtained.

An extensive crystal survey of the Cambridge Structural Database has been carried out to provide hydrogen-bond data for use in drug-design strategies. Previous crystal surveys have generated 1D frequency distributions of hydrogen-bond distances and angles, which are not sufficient to model the hydrogen bond as a ligand-receptor interaction. For each hydrogen-bonding group of interest to the drug designer, geometric hydrogen-bond criteria have been derived. The 3D distribution of complementary atoms about each hydrogen-bonding group has been ascertained by dividing the space about each group into bins of equal volume and counting the number of observed hydrogen-bonding contacts in each bin. Finally, the propensity of each group to form a hydrogen bond has been calculated. Together, these data can be used to predict the potential site points with which a ligand could interact and therefore could be used in molecular-similarity studies, pharmacophore query searching of databases, or de novo design algorithms.

Steric complementarity is a prerequisite for ligand-receptor recognition; this implies that drugs with a common receptor binding site should possess sterically similar binding surfaces. This principle is used as the basis for an automatic and unbiased method that superposes molecules. One molecule is rotated and translated to maximize the overlap between the two molecular surface volumes. A fast grid-based method is used to determine the extent of this overlap, and this is optimized using simulated annealing. Matches with high steric similarity scores are then sorted on the basis of both hydrogen-bond and electrostatic similarity between the matched molecules. Flexible molecules are treated as a set of rigid representative conformers. The algorithm has correctly predicted superpositions between a number of pairs of molecules, according to crystallographic data from ligands that have been co-crystallized at common enzyme binding sites.