Ultra-large library docking for discovering new chemotypes

Finding Constellations in Chemical Space Through Core Analysis

Herein we introduce the constellation plots as a general approach that merges different and complementary molecular representations to enhance the information contained in a visual representation and analysis of chemical space. The method is based on a combination of a sub-structure based representation and classification of compounds with a "classical" coordinate-based representation of chemical space. A distinctive outcome of the method is that organizing the compounds in analog series leads to the formation of groups of molecules, aka "constellations" in chemical space. The novel approach is general and can be used to rapidly identify, for instance, insightful and "bright" Structure-Activity Relationships (StARs) in chemical space that are easy to interpret. This kind of analysis is expected to be especially useful for lead identification in large datasets of unannotated molecules, such as those obtained through high-throughput screening. We demonstrate the application of the method using two datasets of focused inhibitors designed against DNMTs and AKT1.

Mining large databases to find new leads with low similarity to known actives: application to find new DPP-IV inhibitors

Aim: Fragment-based drug design or bioisosteric replacement is used to find new actives with low (or no) similarity to existing ones but requires the synthesis of nonexisting compounds to prove their predicted bioactivity. Protein-ligand docking or pharmacophore screening are alternatives but they can become computationally expensive when applied to very large databases such as ZINC. Therefore, fast strategies are necessary to find new leads in such databases. Materials & methods: We designed a computational strategy to find lead molecules with very low (or no) similarity to existing actives and applied it to DPP-IV. Results: The bioactivity assays confirm that this strategy finds new leads for DPP-IV inhibitors. Conclusion: This computational strategy reduces the time of finding new lead molecules.

Small molecules for modulating the localisation of the water channel aquaporin-2-disease relevance and perspectives for targeting local cAMP signalling

The tight spatial and temporal organisation of cyclic adenosine monophosphate (cAMP) signalling plays a key role in arginine-vasopressin (AVP)-mediated water reabsorption in renal collecting duct principal cells and in a plethora of other processes such as in the control of cardiac myocyte contractility. This review critically discusses in vitro- and cell-based screening strategies for the identification of small molecules that interfere with AVP/cAMP signalling in renal principal cells; it features phenotypic screening and approaches for targeting protein-protein interactions of A-kinase anchoring proteins (AKAPs), which organise local cAMP signalling hubs. The discovery of novel chemical entities for the modulation of local cAMP will not only provide tools for elucidating molecular mechanisms underlying cAMP signalling. Novel chemical entities can also serve as starting points for the development of novel drugs for the treatment of human diseases. Examples illustrate how screening for small molecules can pave the way to novel approaches for the treatment of certain forms of diabetes insipidus, a disease caused by defects in AVP-mediated water reabsorption.

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.

Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design

In medicinal chemistry programs it is key to design and make compounds that are efficacious and safe. This is a long, complex, and difficult multiparameter optimization process, often including several properties with orthogonal trends. New methods for the automated design of compounds against profiles of multiple properties are thus of great value. Here we present a fragment-based reinforcement learning approach based on an actor-critic model, for the generation of novel molecules with optimal properties. The actor and the critic are both modeled with bidirectional long short-term memory (LSTM) networks. The AI method learns how to generate new compounds with desired properties by starting from an initial set of lead molecules and then improving these by replacing some of their fragments. A balanced binary tree based on the similarity of fragments is used in the generative process to bias the output toward structurally similar molecules. The method is demonstrated by a case study showing that 93% of the generated molecules are chemically valid and more than a third satisfy the targeted objectives, while there were none in the initial set.

Molecular pharmacology of metabotropic receptors targeted by neuropsychiatric drugs

Metabotropic receptors are responsible for so-called 'slow synaptic transmission' and mediate the effects of hundreds of peptide and non-peptide neurotransmitters and neuromodulators. Over the past decade or so, a revolution in membrane-protein structural determination has clarified the molecular determinants responsible for the actions of these receptors. This Review focuses on the G protein-coupled receptors (GPCRs) that are targets of neuropsychiatric drugs and shows how insights into the structure and function of these important synaptic proteins are accelerating understanding of their actions. Notably, elucidating the structure and function of GPCRs should enhance the structure-guided discovery of novel chemical tools with which to manipulate and understand these synaptic proteins.

Target 2035: probing the human proteome

Biomedical scientists tend to focus on only a small fraction of the proteins encoded by the human genome despite overwhelming genetic evidence that many understudied proteins are important for human disease. One of the best ways to interrogate the function of a protein and to determine its relevance as a drug target is by using a pharmacological modulator, such as a chemical probe or an antibody. If these tools were available for most human proteins, it should be possible to translate the tremendous advances in genomics into a greater understanding of human health and disease, and catalyze the creation of innovative new medicines. Target 2035 is a global federation for developing and applying new technologies with the goal of creating chemogenomic libraries, chemical probes, and/or functional antibodies for the entire proteome.

A non-beta-lactam antibiotic inhibitor for enterohemorrhagic Escherichia coli O104:H4

The overuse of antibiotics has caused an increased prevalence of drug-resistant bacteria. Bacterial resistance in E. coli is regulated via production of β-lactam-hydrolyzing β-lactamases enzymes. Escherichia coli O104: H4 is a multi-drug resistant strain known to resist β-lactam as well as several other antibiotics. Here, we report a molecular dynamic simulation-combined docking approach to identify, screen, and verify active pharmacophores against enterohemorrhagic Escherichia coli (EHEC). Experimental studies revealed a boronic acid cyclic monomer (BACM), a non-β-lactam compound, to inhibit the growth of E. coli O104: H4. In vitro Kirby Bauer disk diffusion susceptibility testing coupled interaction analysis suggests BACM inhibits E. coli O104:H4 growth by not only inhibiting the β-lactamase pathway but also via direct inhibition of the penicillin-binding protein. These results suggest that BACM could be used as a lead compound to develop potent drugs targeting beta-lactam resistant Gram-negative bacterial strains. KEY MESSAGES: • An in silico approach was reported to identify pharmacophores against E. coli O104: H4. • In vitro studies revealed a non-β-lactam compound to inhibit the growth of E. coli O104: H4. • This non-β-lactam compound could be used as a lead compound for targeting beta-lactam strains.

An outlook on using serial femtosecond crystallography in drug discovery

Introduction: X-ray crystallography has made important contributions to modern drug development but its application to many important drug targets has been extremely challenging. The recent emergence of X-ray free electron lasers (XFELs) and advancements in serial femtosecond crystallography (SFX) have offered new opportunities to overcome limitations of traditional crystallography to accelerate the structure-based drug discovery (SBDD) process. Areas covered: In this review, the authors describe the general principles of X-ray generation and the main properties of XFEL beams, outline details of SFX data collection and processing, and summarize the progress in the development of associated instrumentation for sample delivery and X-ray detection. An overview of the SFX applications to various important drug targets such as membrane proteins is also provided. Expert opinion: While SFX has already made clear advancements toward the understanding of the structure and dynamics of several major drug targets, its robust application in SBDD still needs further developments of new high-throughput techniques for sample production, automation of crystal delivery and data collection, as well as for processing and storage of large amounts of data. The expansion of the available XFEL beamtime is a key to the success of SFX in SBDD.

Interrogating dense ligand chemical space with a forward-synthetic library

Forward-synthetic databases are an efficient way to enumerate chemical space. We explored here whether these databases are good sources of novel protein ligands and how many molecules are obtainable and in which time frame. Based on docking calculations, series of molecules were selected to gain insights into the ligand structure-activity relationship. To evaluate the novelty of compounds in a challenging way, we chose the β₂-adrenergic receptor, for which a large number of ligands is already known. Finding dissimilar ligands is thus the exception rather than the rule. Here we report on the results, the successful synthesis of 127/240 molecules in just 2 weeks, the discovery of previously unreported dissimilar ligands of the β₂-adrenergic receptor, and the optimization of one series to a K _D of 519 nM in only one round. Moreover, the finding that only 3 of 240 molecules had ever been synthesized before indicates that large parts of chemical space are unexplored.

How to Computationally Stack the Deck for Hit-to-Lead Generation: In Silico Molecular Interaction Energy Profiling for de Novo siRNA Guide Strand Surrogate Selection

The Argonaute-2 protein is part of the RNA-induced silencing complex (RISC) and anchors the guide strand of the small interfering RNA (siRNA). The 3'-end of the RNA contains two unpaired nucleotides (3'-overhang) that interact with the PAZ (PIWI/Argonaute/Zwille) domain of the protein. Theoretical and experimental evidence points toward a direct connection between the PAZ/3'-overhang binding affinity and siRNA's potency and specificity. Among the challenges to overcome when deploying siRNA molecules as therapeutics are their ready degradation under physiological conditions and off-target effects. It has been demonstrated that nuclease resistance can be improved via replacement of the dinucleotide overhang by small molecules which retain the interactions of the RNA guide strand with the PAZ domain. Most commonly, nucleotide analogues are used to substitute the siRNA overhang. However, in this study we adopt a de novo approach to its modification. The X-ray structure of human Argonaute-2 PAZ domain served to perform virtual screening and molecular interaction energy profiling (i.e., decomposition of the force field calculated protein-ligand interaction energies) of tailored-to-purpose fragment libraries. The binding of fragments to the PAZ domain was validated experimentally by NMR spectroscopy. The in silico guided protocol led to the efficient discovery of a number of PAZ domain ligands with affinities comparable to that of a reference dinucleotide (UpU, K_d = 33 μM). Originally starting from a generic fragment library, hits progress from 930 μM down to 14 μM within three iterations for the fragments selected via in silico molecular interaction energy profiling from a bespoke library. These dinucleotide siRNA guide strand surrogates represent potential new siRNA-based therapeutics (when attached to siRNA to form bioconjugates) featuring improved efficacy, specificity, stability, and cellular uptake. This project yielded a portfolio of seven patent applications, four of which have been granted to date.

Systems chemical analytics: introduction to the challenges of chemical complexity analysis

We present concepts of complexity, and complex chemistry in systems subjected to biotic and abiotic transformations, and introduce analytical possibilities to disentangle chemical complexity into its elementary parts as a global integrated approach termed systems chemical analytics.

A Strength-Weaknesses-Opportunities-Threats (SWOT) Analysis of Cheminformatics in Natural Product Research

Cheminformatics-based techniques, such as molecular modeling, docking, virtual screening, and machine learning, are well accepted for their usefulness in drug discovery and development of therapeutically relevant small molecules. Although delayed by several decades, their application in natural product research has led to outstanding findings. Combining information obtained from different sources, i.e., virtual predictions, traditional medicine, structural, biochemical, and biological data, and handling big data effectively will open up new possibilities, but also challenges in the future. Strategies and examples will be presented on how to integrate cheminformatics in pharmacognostic workflows to benefit from these two highly complementary disciplines toward streamlining experimental efforts. While considering their limits and pitfalls and by exploiting their potential, computer-aided strategies should successfully guide future studies and thereby augment our knowledge of bioactive natural lead structures.