Finding the sweet spot: the role of nature and nurture in medicinal chemistry

C3 amino-substituted chalcone derivative with selective adenosine rA1 receptor affinity in the micromolar range

ABSTRACT

To identify novel adenosine receptor (AR) ligands based on the chalcone scaffold, herein the synthesis, characterization and in vitro and in silico evaluation of 33 chalcones (15-36 and 37-41) and structurally related compounds (42-47) are reported. These compounds were characterized by radioligand binding and GTP shift assays to determine the degree and type of binding affinity, respectively, against rat (r) A1 and A2A ARs. The chalcone derivatives 24, 29, 37 and 38 possessed selective A1 affinity below 10 µM, and thus, are the most active compounds of the present series; compound 38 was the most potent selective A1 AR antagonist (K i (r) = 1.6 µM). The structure-affinity relationships (SAR) revealed that the NH2-group at position C3 of ring A of the chalcone scaffold played a key role in affinity, and also, the Br-atom at position C3' on benzylidene ring B. Upon in vitro and in silico evaluation, the novel C3 amino-substituted chalcone derivative 38-that contains an α,ß-unsaturated carbonyl system and easily allows structural modification-may possibly be a synthon in future drug discovery.

GRAPHIC ABSTRACT

C3 amino-substituted chalcone derivative (38) with C3' Br substitution on benzylidene ring B possesses selective adenosine rA1 receptor affinity in micromolar range.

Structural and biophysical insights into the mode of covalent binding of rationally designed potent BMX inhibitors

The bone marrow tyrosine kinase in chromosome X (BMX) is pursued as a drug target because of its role in various pathophysiological processes. We designed BMX covalent inhibitors with single-digit nanomolar potency with unexploited topological pharmacophore patterns. Importantly, we reveal the first X-ray crystal structure of covalently inhibited BMX at Cys496, which displays key interactions with Lys445, responsible for hampering ATP catalysis and the DFG-out-like motif, typical of an inactive conformation. Molecular dynamic simulations also showed this interaction for two ligand/BMX complexes. Kinome selectivity profiling showed that the most potent compound is the strongest binder, displays intracellular target engagement in BMX-transfected cells with two-digit nanomolar inhibitory potency, and leads to BMX degradation PC3 in cells. The new inhibitors displayed anti-proliferative effects in androgen-receptor positive prostate cancer cells that where further increased when combined with known inhibitors of related signaling pathways, such as PI3K, AKT and Androgen Receptor. We expect these findings to guide development of new selective BMX therapeutic approaches.

Proteomimetic surface fragments distinguish targets by function

The fragment-centric design promises a means to develop complex xenobiotic protein surface mimetics, but it is challenging to find locally biomimetic structures. To address this issue, foldameric local surface mimetic (LSM) libraries were constructed. Protein affinity patterns, ligand promiscuity and protein druggability were evaluated using pull-down data for targets with various interaction tendencies and levels of homology. LSM probes based on H14 helices exhibited sufficient binding affinities for the detection of both orthosteric and non-orthosteric spots, and overall binding tendencies correlated with the magnitude of the target interactome. Binding was driven by two proteinogenic side chains and LSM probes could distinguish structurally similar proteins with different functions, indicating limited promiscuity. Binding patterns displayed similar side chain enrichment values to those for native protein-protein interfaces implying locally biomimetic behavior. These analyses suggest that in a fragment-centric approach foldameric LSMs can serve as useful probes and building blocks for undruggable protein interfaces.

Consensus-Based Pharmacophore Mapping for New Set of N-(disubstituted-phenyl)-3-hydroxyl-naphthalene-2-carboxamides

A series of twenty-two novel N-(disubstituted-phenyl)-3-hydroxynaphthalene- 2-carboxamide derivatives was synthesized and characterized as potential antimicrobial agents. N-[3,5-bis(trifluoromethyl)phenyl]- and N-[2-chloro-5-(trifluoromethyl)phenyl]-3-hydroxy- naphthalene-2-carboxamide showed submicromolar (MICs 0.16–0.68 µM) activity against methicillin-resistant Staphylococcus aureus isolates. N-[3,5-bis(trifluoromethyl)phenyl]- and N-[4-bromo-3-(trifluoromethyl)phenyl]-3-hydroxynaphthalene-2-carboxamide revealed activity against M. tuberculosis (both MICs 10 µM) comparable with that of rifampicin. Synergistic activity was observed for the combinations of ciprofloxacin with N-[4-bromo-3-(trifluoromethyl)phenyl]- and N-(4-bromo-3-fluorophenyl)-3-hydroxynaphthalene-2-carboxamides against MRSA SA 630 isolate. The similarity-related property space assessment for the congeneric series of structurally related carboxamide derivatives was performed using the principal component analysis. Interestingly, different distribution of mono-halogenated carboxamide derivatives with the –CF₃ substituent is accompanied by the increased activity profile. A symmetric matrix of Tanimoto coefficients indicated the structural dissimilarities of dichloro- and dimetoxy-substituted isomers from the remaining ones. Moreover, the quantitative sampling of similarity-related activity landscape provided a subtle picture of favorable and disallowed structural modifications that are valid for determining activity cliffs. Finally, the advanced method of neural network quantitative SAR was engaged to illustrate the key 3D steric/electronic/lipophilic features of the ligand-site composition by the systematic probing of the functional group.

DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach

We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties.

In vitro Cas9-assisted editing of modular polyketide synthase genes to produce desired natural product derivatives

One major bottleneck in natural product drug development is derivatization, which is pivotal for fine tuning lead compounds. A promising solution is modifying the biosynthetic machineries of middle molecules such as macrolides. Although intense studies have established various methodologies for protein engineering of type I modular polyketide synthase(s) (PKSs), the accurate targeting of desired regions in the PKS gene is still challenging due to the high sequence similarity between its modules. Here, we report an innovative technique that adapts in vitro Cas9 reaction and Gibson assembly to edit a target region of the type I modular PKS gene. Proof-of-concept experiments using rapamycin PKS as a template show that heterologous expression of edited biosynthetic gene clusters produced almost all the desired derivatives. Our results are consistent with the promiscuity of modular PKS and thus, our technique will provide a platform to generate rationally designed natural product derivatives for future drug development.

Several different genetic strategies have been reported for the modification of polyketide synthases but the highly repetitive modular structure makes this difficult. Here the authors report on an adapted Cas9 reaction and Gibson assembly to edit a target region of the polyketide synthases gene in vitro.

Open access in silico tools to predict the ADMET profiling of drug candidates

INTRODUCTION

We are in an era of bioinformatics and cheminformatics where we can predict data in the fields of medicine, the environment, engineering and public health. Approaches with open access in silico tools have revolutionized disease management due to early prediction of the absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiles of the chemically designed and eco-friendly next-generation drugs.

AREAS COVERED

This review meticulously encompasses the fundamental functions of open access in silico prediction tools (webservers and standalone software) and advocates their use in drug discovery research for the safety and reliability of any candidate-drug. This review also aims to help support new researchers in the field of drug design.

EXPERT OPINION

The choice of in silico tools is critically important for drug discovery and the accuracy of ADMET prediction. The accuracy largely depends on the types of dataset, the algorithm used, the quality of the model, the available endpoints for prediction, and user requirement. The key is to use multiple in silico tools for predictions and comparing the results, followed by the identification of the most probable prediction.

Glycosphingolipid expression at breast cancer stem cells after novel thieno[2,3-b]pyridine anticancer compound treatment

Glycosphingolipid expression differs between human breast cancer stem cells (CSC) and cancer non-stem cells (non-CSC). We performed studies of viability, type of cell death, cancer stem cell percent and glycosphingolipid expression on CSC and non-CSC after treatment of MDA-MB-231 and MDA-MB-453 triple-negative breast cancer cells with a newly developed thienopyridine anticancer compound (3-amino-N-(3-chloro-2-methylphenyl)-5-oxo-5,6,7,8-tetrahydrothieno[2,3-b]quinoline-2-carboxamide, 1). Compound 1 was cytotoxic for both breast cancer cell lines and the majority of cells died by treatment-induced apoptosis. The percent of cancer stem cells and number of formed mammospheres was significantly lower. Glycosphingolipids IV⁶Neu5Ac-nLc₄Cer and GalNAc-GM1b (IV³Neu5Ac-Gg5Cer) not reported previously, were identified in both CSCs and non-CSCs. IV⁶Neu5Ac-nLc₄Cer had increased expression in both CSCs and non-CSCs of both cell lines after the treatment with 1, while GM3 (II³Neu5Ac-LacCer) had increased expression only on both cell subpopulations in MDA-MB-231 cell line. GalNAc-GM1b, Gb₄Cer (GalNAcβ1-3Galα1-4Galβ1-4Glcβ1-1Cer) and GM2 (II³Neu5Ac-GalNAcβ1-4Galβ1-4Glcβ1-1Cer) were increased only in CSCs of both cell lines while GD3 was decreased in CSC of MDA-MB-231 cell line. Due to its effect in reducing the percentage of cancer stem cells and number of mammospheres, and its influence upon several glycosphingolipid expressions, it can be concluded that compound 1 deserves attention as a potential new drug for triple-negative breast cancer therapy.

Facts, Patterns, and Principles in Drug Discovery: Appraising the Rule of 5 with Measured Physicochemical Data

The rule of 5 was designed to estimate the likelihood of poor absorption or permeation, noting the impact of poor solubility. This Perspective explores the impact of various physicochemical descriptors and contemporary lipophilicity measurements on permeability and solubility, showing that the distribution coefficient log D_7.4 (rather than log P) is the most impactful parameter. Molecular weight, almost invariably the defining characteristic of "beyond the rule of 5" compounds, has little impact on solubility when log D_7.4 measurements and aromaticity are considered. Predicting permeation is more complex, given passive and carrier transport mechanisms; however, notable patterns of behavior are apparent, giving insight even "beyond the rule of 5". Recommended best practices should involve using the facts (measurements) and the patterns they reveal to establish informative principles rather than fastidious rules.

Yicathins B and C and Analogues: Total Synthesis, Lipophilicity and Biological Activities

Natural products have always been an important source of new hits and leads in drug discovery, with the marine environment being regarded as a significant source of novel and exquisite bioactive compounds. Yicathins B and C are two marine-derived xanthones that have shown antibacterial and antifungal activity. Herein, the total synthesis of these yicathins and six novel analogues is reported for the first time. As marine natural products tend to have very lipophilic scaffolds, the lipophilicity of yicathins and their analogues was evaluated in the classical octanol/water system and a biomimetic model-based system. As the xanthonic nucleus is a "privileged structure", other biological activities were evaluated, namely antitumor and anti-inflammatory activities. An interesting anti-inflammatory activity was identified for yicathin analogues that paves the way for the design of dual activity (anti-infective and anti-inflammatory) marine-inspired xanthone derivatives.

DNA-Encoded Library Screening as Core Platform Technology in Drug Discovery: Its Synthetic Method Development and Applications in DEL Synthesis

DNA-encoded library technology (DELT) was introduced to our medicinal chemistry society more than 20 years ago. The application of DELT in the development of clinical candidates has been actively reported in the literature recently. A few representative examples include RIP1K inhibitors for inflammatory diseases and sEH inhibitors for endothelial dysfunction or abnormal tissue repair, among many others. Here, the authors would like to recall the recent developments in on-DNA synthetic methodologies for DEL construction and to analyze recent examples in the literature of DELT-based drug development efforts pursued in both the academic and industrial sectors. With this perspective, we hope to provide a useful summary of recent DELT-based drug discovery research and to discuss the future scope of DELT in medicinal chemistry.

Alkyne Linchpin Strategy for Drug:Pharmacophore Conjugation: Experimental and Computational Realization of a Meta-Selective Inverse Sonogashira Coupling

The late-stage functionalization (LSF) of pharmaceutical and agrochemical compounds by the site-selective activation of C-H bonds provides access to diverse structural analogs and expands synthetically-accessible chemical space. We report a C-H functionalization LSF strategy that hinges on the use of an alkyne linchpin to assemble conjugates of sp²-rich marketed pharmaceuticals and agrochemicals with sp³-rich 3D fragments and natural products. This is accomplished through a template-assisted inverse Sonogashira reaction that displays high levels of selectivity for the meta position. This protocol is also amenable to distal structural modifications of α-amino acids. The transformation of alkyne functionality to other functional groups further highlights the applicative potential. Computational and experimental mechanistic studies shed light on the detailed mechanism. Turnover-limiting 1,2-migratory insertion of the bromoalkyne coupling partner occurs after relatively fast C-H activation. While this insertion occurs unselectively, regioconvergence results from one of the adducts undergoing a 1,2-trialkylsilyl migration to form the alkynylated product. A heterobimetallic Pd-Ag transition structure is essential for product formation in the β-bromide elimination step.

Thermodynamic profiling for fragment-based lead discovery and optimization

Introduction: The enthalpic and entropic components of the ligand-protein binding free energy reflect the type and quality of the interactions and relate to the physicochemical properties of the ligands. These findings have significance in medicinal chemistry optimizations since they suggest that the thermodynamic profiling of the binding may help monitor and control the unfavorable size and hydrophobicity increase typically accompanying affinity improvements and leading to suboptimal pharmacokinetic properties.Areas covered: This review describes the ligand-protein binding event in terms of elementary steps, their associated interactions, and their enthalpic and entropic consequences. The relationships among the breaking and forming interactions, the binding thermodynamic profile, and the physicochemical properties of the ligands are also discussed.Expert opinion: Analysis of the size dependence of available affinity and favorable enthalpy highlights the limitation of the simultaneous optimization of these quantities. Indeed, moderate, rather than very high affinities can be conciliated with favorable physicochemical and pharmacokinetic profiles as it is supported by the affinity range of historical oral drugs. Although thermodynamic quantities are not suitable endpoints for medicinal chemistry optimizations owing to the complexity of the binding thermodynamics, thermodynamic profiling together with structural studies can be advantageously used to understand the details of the binding process and to optimize it.

A Chemometric Analysis of Deep-Sea Natural Products

Deep-sea natural products have been created by unique marine organisms that thrive in a challenging environment of extreme conditions for its inhabitants. In this study, 179 deep-sea natural products isolated from 2009 to 2013 were investigated by analysing their physicochemical properties that are important indicators of the ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) profile of a compound. The study and analysis of these molecular descriptors and characteristics enabled the defining of these compounds in various chemical spaces, particularly as an indication of their drug-likeness and position in chemical space and is the first to be conducted to analyse deep-sea derived natural products. It was found that ~40% of all deep-sea natural products were drug-like and 2/3 were within Known Drug Space (KDS), highlighting the high drug-likeness of a significant proportion of deep-sea natural products, most of which have already been shown to have notable biological activities, that should be further investigated as potential therapeutics. Furthermore, this study was able to reveal the general structural differences between compounds from Animalia, Bacteria and Fungi organisms where it was observed that natural products from members of the Animalia kingdom are structurally more varied than compounds from bacteria and fungi. It was also noted that, in general, fungi-derived compounds occupy a more favourable position in drug-like chemical space and are a rich and promising source of biologically-active natural products for the purposes of drug development and therapeutic application.

SAR-mediated Similarity Assessment of the Property Profile for New, Silicon-Based AChE/BChE Inhibitors

A set of 25 novel, silicon-based carbamate derivatives as potential acetyl- and butyrylcholinesterase (AChE/BChE) inhibitors was synthesized and characterized by their in vitro inhibition profiles and the selectivity indexes (SIs). The prepared compounds were also tested for their inhibition potential on photosynthetic electron transport (PET) in spinach (Spinacia oleracea L.) chloroplasts. In fact, some of the newly prepared molecules revealed comparable or even better inhibitory activities compared to the marketed drugs (rivastigmine or galanthamine) and commercially applied pesticide Diuron®, respectively. Generally, most compounds exhibited better inhibition potency towards AChE; however, a wider activity span was observed for BChE. Notably, benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(2-hydroxyphenyl)carbamoyl]ethyl]-carbamate (2) and benzyl N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(3-hydroxyphenyl)carbamoyl]ethyl]-carbamate (3) were characterized by fairly high selective indexes. Specifically, compound 2 was prescribed with the lowest IC50 value that corresponds quite well with galanthamine inhibition activity, while the inhibitory profiles of molecules 3 and benzyl-N-[(1S)-2-[(tert-butyldimethylsilyl)oxy]-1-[(4-hydroxyphenyl)carbamoyl]ethyl]carbamate (4) are in line with rivastigmine activity. Moreover, a structure-activity relationship (SAR)-driven similarity evaluation of the physicochemical properties for the carbamates examined appeared to have foreseen the activity cliffs using a similarity-activity landscape index for BChE inhibitory response values. The 'indirect' ligand-based and 'direct' protein-mediated in silico approaches were applied to specify electronic/steric/lipophilic factors that are potentially valid for quantitative (Q)SAR modeling of the carbamate analogues. The stochastic model validation was used to generate an 'average' 3D-QSAR pharmacophore pattern. Finally, the target-oriented molecular docking was employed to (re)arrange the spatial distribution of the ligand property space for BChE and photosystem II (PSII).

2016 Philip S. Portoghese Medicinal Chemistry Lectureship: Designing Bivalent or Bitopic Molecules for G-Protein Coupled Receptors. The Whole Is Greater Than the Sum of Its Parts

The genesis of designing bivalent or bitopic molecules that engender unique pharmacological properties began with Portoghese's work directed toward opioid receptors, in the early 1980s. This strategy has evolved as an attractive way to engineer highly selective compounds for targeted G-protein coupled receptors (GPCRs) with optimized efficacies and/or signaling bias. The emergence of X-ray crystal structures of many GPCRs and the identification of both orthosteric and allosteric binding sites have provided further guidance to ligand drug design that includes a primary pharmacophore (PP), a secondary pharmacophore (SP), and a linker between them. It is critical to note the synergistic relationship among all three of these components as they contribute to the overall interaction of these molecules with their receptor proteins and that strategically designed combinations have and will continue to provide the GPCR molecular tools of the future.

Insights into the EGFR SAR of N-phenylquinazolin-4-amine-derivatives using quantum mechanical pairwise-interaction energies

Protein kinases are an important class of enzymes that play an essential role in virtually all major disease areas. In addition, they account for approximately 50% of the current targets pursued in drug discovery research. In this work, we explore the generation of structure-based quantum mechanical (QM) quantitative structure-activity relationship models (QSAR) as a means to facilitate structure-guided optimization of protein kinase inhibitors. We explore whether more accurate, interpretable QSAR models can be generated for a series of 76 N-phenylquinazolin-4-amine inhibitors of epidermal growth factor receptor (EGFR) kinase by comparing and contrasting them to other standard QSAR methodologies. The QM-based method involved molecular docking of inhibitors followed by their QM optimization within a ~ 300 atom cluster model of the EGFR active site at the M062X/6-31G(d,p) level. Pairwise computations of the interaction energies with each active site residue were performed. QSAR models were generated by splitting the datasets 75:25 into a training and test set followed by modelling using partial least squares (PLS). Additional QSAR models were generated using alignment dependent CoMFA and CoMSIA methods as well as alignment independent physicochemical, e-state indices and fingerprint descriptors. The structure-based QM-QSAR model displayed good performance on the training and test sets (r² ~ 0.7) and was demonstrably more predictive than the QSAR models built using other methods. The descriptor coefficients from the QM-QSAR models allowed for a detailed rationalization of the active site SAR, which has implications for subsequent design iterations.

A Fragmenting Protocol with Explicit Hydration for Calculation of Binding Enthalpies of Target-Ligand Complexes at a Quantum Mechanical Level

Optimization of the enthalpy component of binding thermodynamics of drug candidates is a successful pathway of rational molecular design. However, the large size and missing hydration structure of target-ligand complexes often hinder such optimizations with quantum mechanical (QM) methods. At the same time, QM calculations are often necessitated for proper handling of electronic effects. To overcome the above problems, and help the QM design of new drugs, a protocol is introduced for atomic level determination of hydration structure and extraction of structures of target-ligand complex interfaces. The protocol is a combination of a previously published program MobyWat, an engine for assigning explicit water positions, and Fragmenter, a new tool for optimal fragmentation of protein targets. The protocol fostered a series of fast calculations of ligand binding enthalpies at the semi-empirical QM level. Ligands of diverse chemistry ranging from small aromatic compounds up to a large peptide helix of a molecular weight of 3000 targeting a leukemia protein were selected for systematic investigations. Comparison of various combinations of implicit and explicit water models demonstrated that the presence of accurately predicted explicit water molecules in the complex interface considerably improved the agreement with experimental results. A single scaling factor was derived for conversion of QM reaction heats into binding enthalpy values. The factor links molecular structure with binding thermodynamics via QM calculations. The new protocol and scaling factor will help automated optimization of binding enthalpy in future molecular design projects.

Structural simplification: an efficient strategy in lead optimization

The trend toward designing large hydrophobic molecules for lead optimization is often associated with poor drug-likeness and high attrition rates in drug discovery and development. Structural simplification is a powerful strategy for improving the efficiency and success rate of drug design by avoiding "molecular obesity". The structural simplification of large or complex lead compounds by truncating unnecessary groups can not only improve their synthetic accessibility but also improve their pharmacokinetic profiles, reduce side effects and so on. This review will summarize the application of structural simplification in lead optimization. Numerous case studies, particularly those involving successful examples leading to marketed drugs or drug-like candidates, will be introduced and analyzed to illustrate the design strategies and guidelines for structural simplification.

Identification of C-β-d-Glucopyranosyl Azole-Type Inhibitors of Glycogen Phosphorylase That Reduce Glycogenolysis in Hepatocytes: In Silico Design, Synthesis, in Vitro Kinetics, and ex Vivo Studies

Several C-β-d-glucopyranosyl azoles have recently been uncovered as among the most potent glycogen phosphorylase (GP) catalytic site inhibitors discovered to date. Toward further exploring their translational potential, ex vivo experiments have been performed for their effectiveness in reduction of glycogenolysis in hepatocytes. New compounds for these experiments were predicted in silico where, for the first time, effective ranking of GP catalytic site inhibitor potencies using the molecular mechanics-generalized Born surface area (MM-GBSA) method has been demonstrated. For a congeneric training set of 27 ligands, excellent statistics in terms of Pearson (R_P) and Spearman (R_S) correlations (both 0.98), predictive index (PI = 0.99), and area under the receiver operating characteristic curve (AU-ROC = 0.99) for predicted versus experimental binding affinities were obtained, with ligand tautomeric/ionization states additionally considered using density functional theory (DFT). Seven 2-aryl-4(5)-(β-d-glucopyranosyl)-imidazoles and 2-aryl-4-(β-d-glucopyranosyl)-thiazoles were subsequently synthesized, and kinetics experiments against rabbit muscle GPb revealed new potent inhibitors with best K_i values in the low micromolar range (5c = 1.97 μM; 13b = 4.58 μM). Ten C-β-d-glucopyranosyl azoles were then tested ex vivo in mouse primary hepatocytes. Four of these (5a-c and 9d) demonstrated significant reduction of glucagon stimulated glycogenolysis (IC₅₀ = 30-60 μM). Structural and predicted physicochemical properties associated with their effectiveness were analyzed with permeability related parameters identified as crucial factors. The most effective ligand series 5 contained an imidazole ring, and the calculated pK_a (Epik: 6.2; Jaguar 5.5) for protonated imidazole suggests that cellular permeation through the neutral state is favored, while within the cell, there is predicted more favorable binding to GP in the protonated form.

Substituted Aminoacetamides as Novel Leads for Malaria Treatment

Herein we describe the optimization of a phenotypic hit against Plasmodium falciparum based on an aminoacetamide scaffold. This led to N-(3-chloro-4-fluorophenyl)-2-methyl-2-{[4-methyl-3-(morpholinosulfonyl)phenyl]amino}propanamide (compound 28) with low-nanomolar activity against the intraerythrocytic stages of the malaria parasite, and which was found to be inactive in a mammalian cell counter-screen up to 25 μm. Inhibition of gametes in the dual gamete activation assay suggests that this family of compounds may also have transmission blocking capabilities. Whilst we were unable to optimize the aqueous solubility and microsomal stability to a point at which the aminoacetamides would be suitable for in vivo pharmacokinetic and efficacy studies, compound 28 displayed excellent antimalarial potency and selectivity; it could therefore serve as a suitable chemical tool for drug target identification.

Ligand potency - an essential estimator for drug design: between intuition, misinterpretation and serendipity

This is a review of developments in the field of potency, which is an essential estimator for drug design. We discuss the basic concepts of research and discovery, focusing on how misinterpretation in this area, - but also intuition and serendipity, which are common in drug design - helped us to pave a bumpy road towards better drugs. How far we still are from this goal can be seen in the Eroom law, which states that the efficiency of pharma research and development is decreasing. At the same time, pharma bestsellers are getting older.

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.

Exploring the Chemical Space of Cytochrome P450 Inhibitors Using Integrated Physicochemical Parameters, Drug Efficiency Metrics and Decision Tree Models

The cytochrome P450s (CYPs) play a central role in the metabolism of various endogenous and exogenous compounds including drugs. CYPs are vulnerable to inhibition and induction which can lead to adverse drug reactions. Therefore, insights into the underlying mechanism of CYP450 inhibition and the estimation of overall CYP inhibitor properties might serve as valuable tools during the early phases of drug discovery. Herein, we present a large data set of inhibitors against five major metabolic CYPs (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) for the evaluation of important physicochemical properties and ligand efficiency metrics to define property trends across various activity levels (active, efficient and inactive). Decision tree models for CYP inhibition were developed with an accuracy >90% for both the training set and 10-folds cross validation. Overall, molecular weight (MW), hydrogen bond acceptors/donors (HBA/HBD) and lipophilicity (clogP/logPo/w) represent important physicochemical descriptors for CYP450 inhibitors. However, highly efficient CYP inhibitors show mean MW, HBA, HBD and logP values between 294.18–482.40,5.0–8.2,1–7.29 and 1.68–2.57, respectively. Our results might help in optimization of toxicological profiles associated with new chemical entities (NCEs), through a better understanding of inhibitor properties leading to CYP-mediated interactions.

In silico toxicity as a tool for harm reduction: A study of new psychoactive amphetamines and cathinones in the context of criminal science

The emergence of new psychoactive substances (NPS) has raised many issues in the context of law enforcement and public drug policies. In this scenario, interdisciplinary studies are crucial to the decision-making process in the field of criminal science. Unfortunately, information about how NPS affect people's health is lacking even though knowledge about the toxic potential of these substances is essential: the more information about these drugs, the greater the possibility of avoiding damage within the scope of a harm reduction policy. Traditional analytical methods may be inaccessible in the field of forensic science because they are relatively expensive and time-consuming. In this sense, less costly and faster in silico methodologies can be useful strategies. In this work, we submitted computer-calculated toxicity values of various amphetamines and cathinones to an unsupervised multivariate analysis, namely Principal Component Analysis (PCA), and to the supervised techniques Soft Independent Modeling of Class Analogy and Partial Least Square-Discriminant Analysis (SIMCA and PLS-DA) to evaluate how these two NPS groups behave. We studied how theoretical and experimental values are correlated by PLS regression. Although experimental data was available for a small amount of molecules, correlation values reproduced literature values. The in silico method efficiently provided information about the drugs. On the basis of our findings, the technical information presented here can be used in decision-making regarding harm reduction policies and help to fulfill the objectives of criminal science.

Antibodies and venom peptides: new modalities for ion channels

Ion channels play fundamental roles in both excitable and non-excitable tissues and therefore constitute attractive drug targets for myriad neurological, cardiovascular and metabolic diseases as well as for cancer and immunomodulation. However, achieving selectivity for specific ion channel subtypes with small-molecule drugs has been challenging, and there currently is a growing trend to target ion channels with biologics. One approach is to improve the pharmacokinetics of existing or novel venom-derived peptides. In parallel, after initial studies with polyclonal antibodies demonstrated the technical feasibility of inhibiting channel function with antibodies, multiple preclinical programmes are now using the full spectrum of available technologies to generate conventional monoclonal and engineered antibodies or nanobodies against extracellular loops of ion channels. After a summary of the current state of ion channel drug discovery, this Review discusses recent developments using the purinergic receptor channel P2X purinoceptor 7 (P2X7), the voltage-gated potassium channel KV1.3 and the voltage-gated sodium channel NaV1.7 as examples of targeting ion channels with biologics.

A Chemometric Analysis of Compounds from Native New Zealand Medicinal Flora

Several hundred (396) compounds from New Zealand flora with medicinal properties were analyzed for their physicochemical properties. It was found that approximately 10 % fulfilled all the requirements to be considered to be lead-like, over half of the compounds were deemed to be in the drug-like space and ≈75 % were in the known drug space. These results indicate the presence of a significant proportion of compounds that are of particular interest to pursue as potential lead compounds or therapeutics. Additionally, compound classes were analyzed separately-most carbonyl-containing compounds (aldehydes, ketones, esters and lactones), along with phenols were the most lead-like compounds, which also displayed very good proportions in the drug-like and known drug space. The information presented herein can be harnessed and utilized in future work, through focussing on the compounds and compound classes that exhibit high-levels of lead-likeness for further development.

Ranking Hits From Saturation Transfer Difference Nuclear Magnetic Resonance-Based Fragment Screening

Fragment-based screening is an established route to identify low-molecular-weight molecules to generate high-affinity inhibitors in drug discovery. The affinities of these early hits from fragment screenings require a highly sensitive biophysical screening technique. Saturation transfer difference (STD) nuclear magnetic resonance (NMR) is one of the most popular methods owing to its high sensitivity for low-affinity ligands. It would be highly beneficial if rank-ordering of hits according to their affinity from an initial or counter-screen could be performed—a selection criterion found in the literature. We applied Complete Relaxation and Conformational Exchange Matrix (CORCEMA) theory adapted for saturation transfer (ST) measurements (CORCEMA-ST) calculations to predict STD NMR results from a large set of fragment/receptor pairs to investigate the boundaries under which the assumption holds true that a high STD effect can be applied to select for higher-affinity fragments. Overall, we come to the conclusion that this assumption is invalid.

Electrostatic Complementarity as a Fast and Effective Tool to Optimize Binding and Selectivity of Protein-Ligand Complexes

Electrostatic interactions between small molecules and their respective receptors are essential for molecular recognition and are also key contributors to the binding free energy. Assessing the electrostatic match of protein-ligand complexes therefore provides important insights into why ligands bind and what can be changed to improve binding. Ideally, the ligand and protein electrostatic potentials at the protein-ligand interaction interface should maximize their complementarity while minimizing desolvation penalties. In this work, we present a fast and efficient tool to calculate and visualize the electrostatic complementarity (EC) of protein-ligand complexes. We compiled benchmark sets demonstrating electrostatically driven structure-activity relationships (SAR) from literature data, including kinase, protein-protein interaction, and GPCR targets, and used these to demonstrate that the EC method can visualize, rationalize, and predict electrostatically driven ligand affinity changes and help to predict compound selectivity. The methodology presented here for the analysis of EC is a powerful and versatile tool for drug design.

DFT and QSAR investigations of substituent effects in pyrazolooxazine derivatives: Activity prediction

The theoretical investigation of the physico-chemical properties of pyrazolooxazine derivatives, which could exhibit anti-inflammatory activity, has been carried out using DFT computations at the B3LYP/6-311[Formula: see text]G (d, p) level. It appears that both pyrazolooxazin-2-one (1) and pyrazolooxazin-2-thione (2) exhibit thermal stability whereas the electron donating character of the latter is higher than that of the former species. Molecular electrostatic potential surfaces (MESP) have been used in order to determine the activity sites of these species. The HSAB (Hard and Soft Acids and Bases) principle helped to determine the reactivity as well as the effect of substituting groups on the molecules. The stability of these compounds has been analyzed using natural bond orbital analyses NBO. Then, the structure-activity properties of a large series of derivatives were investigated using the HyperChem 8.0.6 software considering the Lipinski’s “rule of five”. The results of QSAR analyses showed that the most promising compound among others, named f1 (a thiophen ring being attached to the nitrogen atom 3 of 1) should be a good candidate for experimental testing.

Structural Simplification of Natural Products

Natural products (NPs) are important sources of clinical drugs due to their structural diversity and biological prevalidation. However, the structural complexity of NPs leads to synthetic difficulties, unfavorable pharmacokinetic profiles, and poor drug-likeness. Structural simplification by truncating unnecessary substructures is a powerful strategy for overcoming these limitations and improving the efficiency and success rate of NP-based drug development. Herein, we will provide a comprehensive review of the structural simplification of NPs with a focus on design strategies, case studies, and new technologies. In particular, a number of successful examples leading to marketed drugs or drug candidates will be discussed in detail to illustrate how structural simplification is applied in lead optimization of NPs.

Broad-scale analysis of thermodynamic signatures in medicinal chemistry: are enthalpy-favored binders the better development option?

Thermodynamic profiles of ligand binding, particularly enthalpically favored binding signatures, have been suggested as a criterion to support the decision-making process around which compounds to select for further optimization in drug development. The concept was enthusiastically taken up, but turned out to be too superficial, either because many aspects determining thermodynamic profiles are insufficiently appreciated or because it is difficult to compare such data on a global scale. The impact of water, changes in protonation states, along with buffer dependencies and incompatible measurement conditions that are far from standard conditions hamper such broad-scale comparisons. However, thermodynamic signatures can make us aware of the impact of these aspects and provide important hints for improving our understanding of the binding process and defining criteria for drug optimization.

Chikungunya virus inhibition by synthetic coumarin-guanosine conjugates

Since its discovery in Tanganyika, Africa in 1952, chikungunya virus (CHIKV) outbreaks have occurred in Africa, Asia, Europe, and America. Till now chikungunya fever has spread in nearly 40 countries. Because of lack of effective vaccines and antiviral drugs to intervene this disease, 21 new conjugated compounds were designed and synthesized by coupling of 6,8-dithioguanosine at its C-6 position with 3-(chloromethyl)coumarins bearing an F, Cl, Br, Me, or -OMe substituent through the -SCH₂- joint. Meanwhile, an organic "dummy" ligand (e.g., methyl, benzyl, and naphthylmethyl) or a coumarinyl moiety was attached at the C-8 position. By high through-put screening, three of these new conjugates were found to inhibit CHIKV in Vero cells with significant potency (EC₅₀ = 9.9-13.9 μM) and showed low toxicity (CC₅₀ = 96.5-212 μM). The selectivity index values were 9.37-21.7. Their structure-activity relationship was deduced, which indicates that the coumarin moiety is essential and the presence of a -OMe group enhances the antiviral activity.

Structures, Activities and Drug-Likeness of Anti-Infective Xanthone Derivatives Isolated from the Marine Environment: A Review

Marine organisms represent almost half of total biodiversity and are a very important source of new bioactive substances. Within the varied biological activities found in marine products, their antimicrobial activity is one of the most relevant. Infectious diseases are responsible for high levels of morbidity and mortality and many antimicrobials lose their effectiveness with time due to the development of resistance. These facts justify the high importance of finding new, effective and safe anti-infective agents. Among the variety of biological activities of marine xanthone derivatives, one that must be highlighted is their anti-infective properties. In this work, a literature review of marine xanthones with anti-infective activity, namely antibacterial, antifungal, antiparasitic and antiviral, is presented. Their structures, biological activity, sources and the methods used for bioactivity evaluation are described. The xanthone derivatives are grouped in three sets: xanthones, hydroxanthones and glycosylated derivatives. Moreover, molecular descriptors, biophysico-chemical properties, and pharmacokinetic parameters were calculated, and the chemical space occupied by marine xanthone derivatives is recognized. The chemical space was compared with marketed drugs and framed accordingly to the drug-likeness concept in order to profile the pharmacokinetic of anti-infective marine xanthone derivatives.

An Application of Fit Quality to Screen MDM2/p53 Protein-Protein Interaction Inhibitors

The judicious application of ligand or binding efficiency (LE) metrics, which quantify the molecular properties required to obtain binding affinity for a drug target, is gaining traction in the selection and optimization of fragments, hits and leads. Here we report for the first time the use of LE based metric, fit quality (FQ), in virtual screening (VS) of MDM2/p53 protein-protein interaction inhibitors (PPIIs). Firstly, a Receptor-Ligand pharmacophore model was constructed on multiple MDM2/ligand complex structures to screen the library. The enrichment factor (EF) for screening was calculated based on a decoy set to define the screening threshold. Finally, 1% of the library, 335 compounds, were screened and re-filtered with the FQ metric. According to the statistical results of FQ vs. activity of 156 MDM2/p53 PPIIs extracted from literatures, the cut-off was defined as FQ = 0.8. After the second round of VS, six compounds with the FQ > 0.8 were picked out for assessing their antitumor activity. At the cellular level, the six hits exhibited a good selectivity (larger than 3) against HepG2 (wt-p53) vs. Hep3B (p53 null) cell lines. On the further study, the six hits exhibited an acceptable affinity (range of K_i from 10² to 10³ nM) to MDM2 when comparing to Nutlin-3a. Based on our work, FQ based VS strategy could be applied to discover other PPIIs.

Removal of Cd(II) and Fe(III) from DMSO by silica gel supported PAMAM dendrimers: Equilibrium, thermodynamics, kinetics and mechanism

A series of silica gel supported amino-terminated PAMAM dendrimers (SG-G1.0 - SG-G3.0) were used for the removal of Cd(II) and Fe(III) from dimethylsulfoxide (DMSO). Various parameters that influence adsorption behaviors including temperature, contact time, and initial metal ion concentration were studied. The adsorption mechanism was revealed by combining the results of experiment and density functional theory (DFT) calculation. It indicates that the adsorption capacities for Cd(II) and Fe(III) are largest among the metal ions tested. The adsorption capacity of SG-G1.0 - SG-3.0 for Cd(II) and Fe(III) follows the order of SG-G2.0 > SG-3.0 > SG-G1.0. The adsorption isotherm shows the adsorption capacities for both metal ions increases with raising the temperature and initial metal ion concentration. The adsorption isotherm is consistent with Langmuir model and the adsorption process is dominated by chemical adsorption mechanism. Thermodynamic parameters indicates that the adsorption for both Cd(II) and Fe(III) is spontaneous and endothermic. Kinetic adsorption indicates that the adsorption equilibrium times for Cd(II) and Fe(III) is about 200 and 350 min, respectively, which can be described by a pseudo-second-order model and controlled by film diffusion process. FTIR analysis and theoretical calculation revealed that the carbonyl O atoms, secondary amine N atoms, and primary amine N atoms are the primary factor responsible for PAMAM adsorption by forming tetra- and penta-coordinated chelates with metal ions.

A therapeutic approach to pantothenate kinase associated neurodegeneration

Pantothenate kinase (PANK) is a metabolic enzyme that regulates cellular coenzyme A (CoA) levels. There are three human PANK genes, and inactivating mutations in PANK2 lead to pantothenate kinase associated neurodegeneration (PKAN). Here we performed a library screen followed by chemical optimization to produce PZ-2891, an allosteric PANK activator that crosses the blood brain barrier. PZ-2891 occupies the pantothenate pocket and engages the dimer interface to form a PANK•ATP•Mg2+•PZ-2891 complex. The binding of PZ-2891 to one protomer locks the opposite protomer in a catalytically active conformation that is refractory to acetyl-CoA inhibition. Oral administration of PZ-2891 increases CoA levels in mouse liver and brain. A knockout mouse model of brain CoA deficiency exhibited weight loss, severe locomotor impairment and early death. Knockout mice on PZ-2891 therapy gain weight, and have improved locomotor activity and life span establishing pantazines as novel therapeutics for the treatment of PKAN.

Probability-driven 3D pharmacophore mapping of antimycobacterial potential of hybrid molecules combining phenylcarbamoyloxy and N-arylpiperazine fragments

The current study examines in silico characterization of the structure-inhibitory potency for a set of phenylcarbamic acid derivatives containing an N-arylpiperazine scaffold, considering the electronic, steric and lipophilic properties. The main objective of the ligand-based modelling was the systematic study of classical comparative molecular field analysis (CoMFA)/comparative molecular surface analysis (CoMSA) performance for the modelling of in vitro efficiency observed for these phenylcarbamates, revealing their inhibitory activities against a virulent Mycobacterium tuberculosis H₃₇R_v strain. We compared the findings of efficiency modelling produced by a standard 3D methodology (CoMFA) and its neural counterparts (CoMSA) regarding multiple training/test subsets and variables used. Moreover, systematic space inspection, splitting values into the analysed training/test subsets, was performed to monitor statistical estimator performance while mapping the probability-driven pharmacophore pattern. Consequently, a 'pseudo-consensus' 3D-quantitative structure-activity relationship (3D-QSAR) approach was applied to retrieve an 'average' pharmacophore hypothesis by the investigation of the most densely populated training/test subpopulations to specify the potentially important factors contributing to the inhibitory activity of phenylcarbamic acid analogues. In addition, examination of descriptor-based similarity with a principal component analysis (PCA) procedure was employed to visualize noticeable variations in the performance of these molecules with respect to their structure and activity profiles.

Towards Intelligent Drug Design System: Application of Artificial Dipeptide Receptor Library in QSAR-Oriented Studies

The pharmacophore properties of a new series of potential purinoreceptor (P2X) inhibitors determined using a coupled neural network and the partial least squares method with iterative variable elimination (IVE-PLS) are presented in a ligand-based comparative study of the molecular surface by comparative molecular surface analysis (CoMSA). Moreover, we focused on the interpretation of noticeable variations in the potential selectiveness of interactions of individual inhibitor-receptors due to their physicochemical properties; therefore, the library of artificial dipeptide receptors (ADP) was designed and examined. The resulting library response to individual inhibitors was arranged in the array, preprocessed and transformed by the principal component analysis (PCA) and PLS procedures. A dominant absolute contribution to PC1 of the Glu attached to heptanoic gating acid and Phe bonded to the linker m-phenylenediamine/triazine scaffold was revealed by the PCA. The IVE-PLS procedure indicated the receptor systems with predominant Pro bonded to the linker and Glu, Gln, Cys and Val directly attached to the gating acid. The proposed comprehensive ligand-based and simplified structure-based methodology allows the in-depth study of the performance of peptide receptors against the tested set of compounds.

Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning

Increase in global population and growing disease burden due to the emergence of infectious diseases (Zika virus), multidrug-resistant pathogens, drug-resistant cancers (cisplatin-resistant ovarian cancer) and chronic diseases (arterial hypertension) necessitate effective therapies to improve health outcomes. However, the rapid increase in drug development cost demands innovative and sustainable drug discovery approaches. Drug repositioning, the discovery of new or improved therapies by reevaluation of approved or investigational compounds, solves a significant gap in the public health setting and improves the productivity of drug development. As the number of drug repurposing investigations increases, a new opportunity has emerged to understand factors driving drug repositioning through systematic analyses of drugs, drug targets and associated disease indications. However, such analyses have so far been hampered by the lack of a centralized knowledgebase, benchmarking data sets and reporting standards. To address these knowledge and clinical needs, here, we present RepurposeDB, a collection of repurposed drugs, drug targets and diseases, which was assembled, indexed and annotated from public data. RepurposeDB combines information on 253 drugs [small molecules (74.30%) and protein drugs (25.29%)] and 1125 diseases. Using RepurposeDB data, we identified pharmacological (chemical descriptors, physicochemical features and absorption, distribution, metabolism, excretion and toxicity properties), biological (protein domains, functional process, molecular mechanisms and pathway cross talks) and epidemiological (shared genetic architectures, disease comorbidities and clinical phenotype similarities) factors mediating drug repositioning. Collectively, RepurposeDB is developed as the reference database for drug repositioning investigations. The pharmacological, biological and epidemiological principles of drug repositioning identified from the meta-analyses could augment therapeutic development.