Methods for building QSARs

Development and Validation of Predictive Quantitative Structure-Activity Relationship Models for Estrogenic Activities of Hydroxylated Polychlorinated Biphenyls

Disruption of the endocrine system by hydroxylated polychlorinated biphenyls (OH-PCBs) is hypothesized, among other potential mechanisms, to be mediated via nuclear receptor binding. Due to the high cost and lengthy time required to produce high-quality experimental data, empirical data to support the nuclear receptor binding hypothesis are in short supply. In the present study, two quantitative structure-activity relationship models were developed for predicting the estrogenic activities of OH-PCBs. Findings revealed that model I (for the estrogen receptor α dataset) contained five two-dimensional (2D) descriptors belonging to the classes autocorrelation, Burden modified eigenvalues, chi path, and atom type electrotopological state, whereas model II (for the estrogen receptor β dataset) contained three 2D and three 3D descriptors belonging to the classes autocorrelation, atom type electrotopological state, and Radial Distribution Function descriptors. The internal and external validation metrics reported for models I and II indicate that both models are robust, reliable, and suitable for predicting the estrogenic activities of untested OH-PCB congeners. Environ Toxicol Chem 2023;42:823-834. © 2023 SETAC.

Predicting mosquito repellents for clothing application from molecular fingerprint-based artificial neural network SAR models

Spraying repellents on clothing limits toxicity and allergy problems that can occur when the repellents are directly applied to skin. This also allows the use of higher doses to ensure longer lasting effects. As the number of repellents available on the market is limited, it is necessary to propose new ones, especially by using in silico methods that reduce costs and time. In this context SAR models were built from a dataset of 2027 chemicals for which repellent activity on clothing was measured against Aedes aegypti. The interest of using either the ECFP or MACCS fingerprints as input neurons of a three-layer perceptron was evaluated. Transformation of MACCS bit strings into disjunctive tables led to interesting results. Models obtained with both types of fingerprints were compared to a model including physicochemical and topological descriptors.

In silico prediction of mosquito repellents for clothing application

Use of protective clothing is a simple and efficient way to reduce the contacts with mosquitoes and consequently the probability of transmission of diseases spread by them. This mechanical barrier can be enhanced by the application of repellents. Unfortunately the number of available repellents is limited. As a result, there is a crucial need to find new active and safer molecules repelling mosquitoes. In this context, a structure-activity relationship (SAR) model was proposed for the design of repellents active on clothing. It was computed from a dataset of 2027 chemicals for which repellent activity on clothing was measured against Aedes aegypti. Molecules were described by means of 20 molecular descriptors encoding physicochemical properties, topological information and structural features. A three-layer perceptron was used as statistical tool. An accuracy of 87% was obtained for both the training and test sets. Most of the wrong predictions can be explained. Avenues for increasing the performances of the model have been proposed.

Modern Approaches in the Discovery and Development of Plant-Based Natural Products and Their Analogues as Potential Therapeutic Agents

Natural products represents an important source of new lead compounds in drug discovery research. Several drugs currently used as therapeutic agents have been developed from natural sources; plant sources are specifically important. In the past few decades, pharmaceutical companies demonstrated insignificant attention towards natural product drug discovery, mainly due to its intrinsic complexity. Recently, technological advancements greatly helped to address the challenges and resulted in the revived scientific interest in drug discovery from natural sources. This review provides a comprehensive overview of various approaches used in the selection, authentication, extraction/isolation, biological screening, and analogue development through the application of modern drug-development principles of plant-based natural products. Main focus is given to the bioactivity-guided fractionation approach along with associated challenges and major advancements. A brief outline of historical development in natural product drug discovery and a snapshot of the prominent natural drugs developed in the last few decades are also presented. The researcher’s opinions indicated that an integrated interdisciplinary approach utilizing technological advances is necessary for the successful development of natural products. These involve the application of efficient selection method, well-designed extraction/isolation procedure, advanced structure elucidation techniques, and bioassays with a high-throughput capacity to establish druggability and patentability of phyto-compounds. A number of modern approaches including molecular modeling, virtual screening, natural product library, and database mining are being used for improving natural product drug discovery research. Renewed scientific interest and recent research trends in natural product drug discovery clearly indicated that natural products will play important role in the future development of new therapeutic drugs and it is also anticipated that efficient application of new approaches will further improve the drug discovery campaign.

The Role of Natural Products as Sources of Therapeutic Agents for Innovative Drug Discovery

Emerging threats to human health require a concerted effort in search of both preventive and treatment strategies, placing natural products at the center of efforts to obtain new therapies and reduce disease spread and associated mortality. The therapeutic value of compounds found in plants has been known for ages, resulting in their utilization in homes and in clinics for the treatment of many ailments ranging from common headache to serious conditions such as wounds. Despite the advancement observed in the world, plant based medicines are still being used to treat many pathological conditions or are used as alternatives to modern medicines. In most cases, these natural products or plant-based medicines are used in an un-purified state as extracts. A lot of research is underway to identify and purify the active compounds responsible for the healing process. Some of the current drugs used in clinics have their origins as natural products or came from plant extracts. In addition, several synthetic analogues are natural product-based or plant-based. With the emergence of novel infectious agents such as the SARS-CoV-2 in addition to already burdensome diseases such as diabetes, cancer, tuberculosis and HIV/AIDS, there is need to come up with new drugs that can cure these conditions. Natural products offer an opportunity to discover new compounds that can be converted into drugs given their chemical structure diversity. Advances in analytical processes make drug discovery a multi-dimensional process involving computational designing and testing and eventual laboratory screening of potential drug candidates. Lead compounds will then be evaluated for safety, pharmacokinetics and efficacy. New technologies including Artificial Intelligence, better organ and tissue models such as organoids allow virtual screening, automation and high-throughput screening to be part of drug discovery. The use of bioinformatics and computation means that drug discovery can be a fast and efficient process and enable the use of natural products structures to obtain novel drugs. The removal of potential bottlenecks resulting in minimal false positive leads in drug development has enabled an efficient system of drug discovery. This review describes the biosynthesis and screening of natural products during drug discovery as well as methods used in studying natural products.

Isoflavones and Bioactivities in Over-fermented Tempeh Extracts

Tempeh is nutritious food prepared through solid-state fermentation of cooked and dehulled soybeans with Rhizopus sp. for about 48 h. Fermentation beyond 48 h resulted in over-fermented tempeh. There may or may not have been similar research done before, especially related to its antioxidant and cytotoxicity. This study aims to determine the characteristics of fermented tempeh for up to 156 h. Samples were fermented at 0, 24, 48, 60, 72, 84, 96, 108, 120, and 156 h. Samples were dried, grounded, and extracted with acetone, followed by defatting with n‑hexane. Extracts were dissolved in organic solvents for free radical scavenging activity (FRSA) and cytotoxicity assays. The 120-h tempeh extract, at the concentration of 1,000 μg/mL, demonstrated the highest FRSA (81.31% inhibition) against 100 µM 2,2-diphenyl-1-picrylhydrazyl (DPPH) solution. Meanwhile, the 108-h tempeh extract at 1,000 μg/mL possessed the highest cytotoxicity (IC50 of 2.54 μg/mL) against MCF-7 breast cancer cell lines. Liquid Chromatography-Mass Spectrometry/Mass Spectroscopy (LC-MS/MS) analysis revealed the presence of daidzin, genistin, daidzein, and genistein in all extracts. Extracts prepared from 108 h and 120 h tempeh stood out from other extracts in the Partial Least Square (PLS) bi-plot due to their high percentage of inhibition, low response of daidzin, and high responses of the other three isoflavones. The cytotoxicity assays of the standard isoflavones showed that genistein had the lowest IC50 value at 4.82 ± 0.11 μg/mL. Standard genistein showed a low percentage of inhibition at 29.79 ± 9.10.

Towards Property Profiling: SYNTHESIS and SAR Probing of New Tetracyclic Diazaphenothiazine Analogues

A series of new tertiary phenothiazine derivatives containing a quinoline and a pyridine fragment was synthesized by the reaction of 1-methyl-3-benzoylthio-4-butylthioquinolinium chloride with 3-aminopyridine derivatives bearing various substituents on the pyridine ring. The direction and mechanism of the cyclization reaction of intermediates with the structure of 1-methyl-4-(3-pyridyl)aminoquinolinium-3-thiolate was related to the substituents in the 2- and 4-pyridine position. The structures of the compounds were analyzed using ¹H, ¹³C NMR (COSY, HSQC, HMBC) and X-ray analysis, respectively. Moreover, the antiproliferative activity against tumor cells (A549, T47D, SNB-19) and a normal cell line (NHDF) was tested. The antibacterial screening of all the compounds was conducted against the reference and quality control strain Staphylococcus aureus ATCC 29213, three clinical isolates of methicillin-resistant S. aureus (MRSA). In silico computation of the intermolecular similarity was performed using principal component analysis (PCA) and hierarchical clustering analysis (HCA) on the pool of structure/property-related descriptors calculated for the novel tetracyclic diazaphenothiazine derivatives. The distance-oriented property evaluation was correlated with the experimental anticancer activities and empirical lipophilicity as well. The quantitative shape-based comparison was conducted using the CoMSA method in order to indicate the potentially valid steric, electronic and lipophilic properties. Finally, the numerical sampling of similarity-related activity landscape (SALI) provided a subtle picture of the SAR trends.

First molecular modelling report on tri-substituted pyrazolines as phosphodiesterase 5 (PDE5) inhibitors through classical and machine learning based multi-QSAR analysis

Phosphodiesterase 5 (PDE5) falls under a broad category of metallohydrolase enzymes responsible for the catalysis of the phosphodiesterase bond, and thus it can terminate the action of cyclic guanosine monophosphate (cGMP). Overexpression of this enzyme leads to development of a number of pathological conditions. Thus, targeting the enzyme to develop inhibitors could be useful for the treatment of erectile dysfunction as well as pulmonary hypertension. In the current study, several molecular modelling techniques were utilized including Bayesian classification, single tree and forest tree recursive partitioning, and genetic function approximation to identify crucial structural fingerprints important for optimization of tri-substituted pyrazoline derivatives as PDE5 inhibitors. Later, various machine learning models were also developed that could be utilized to predict and screen PDE5 inhibitors in the future.

Insight into the structural requirement of aryl sulphonamide based gelatinases (MMP-2 and MMP-9) inhibitors - Part I: 2D-QSAR, 3D-QSAR topomer CoMFA and Naïve Bayes studies - First report of 3D-QSAR Topomer CoMFA analysis for MMP-9 inhibitors and jointly inhibitors of gelatinases together

Gelatinases [gelatinase A - matrix metalloproteinase-2 (MMP-2), gelatinase B - matrix metalloproteinase-9 (MMP-9)] play key roles in many disease conditions including cancer. Despite some research work on gelatinases inhibitors both jointly and individually had been reported, challenges still exist in achieving potency as well as selectivity. Here in part I of a series of work, we have reported the structural requirement of some arylsulfonamides. In particular, regression-based 2D-QSARs, topomer CoMFA (comparative molecular field analysis) and Bayesian classification models were constructed to refine structural features for attaining better gelatinase inhibitory activity. The 2D-QSAR models exhibited good statistical significance. The descriptors nsssN, SHBint6, SHBint7, PubchemFP629 were directly correlated with the MMP-2 binding affinities whereas nsssN, SHBint10 and AATS2i were directly proportional to MMP-9 binding affinities. The topomer CoMFA results indicated that the steric and electrostatic fields play key roles in gelatinase inhibition. The established Naïve Bayes prediction models were evaluated by fivefold cross validation and an external test set. Furthermore, important molecular descriptors related to MMP-2 and MMP-9 binding affinities and some active/inactive fragments were identified. Thus, these observations may be helpful for further work of aryl sulphonamide based gelatinase inhibitors in future.

Two Decades of 4D-QSAR: A Dying Art or Staging a Comeback?

A key question confronting computational chemists concerns the preferable ligand geometry that fits complementarily into the receptor pocket. Typically, the postulated ‘bioactive’ 3D ligand conformation is constructed as a ‘sophisticated guess’ (unnecessarily geometry-optimized) mirroring the pharmacophore hypothesis—sometimes based on an erroneous prerequisite. Hence, 4D-QSAR scheme and its ‘dialects’ have been practically implemented as higher level of model abstraction that allows the examination of the multiple molecular conformation, orientation and protonation representation, respectively. Nearly a quarter of a century has passed since the eminent work of Hopfinger appeared on the stage; therefore the natural question occurs whether 4D-QSAR approach is still appealing to the scientific community? With no intention to be comprehensive, a review of the current state of art in the field of receptor-independent (RI) and receptor-dependent (RD) 4D-QSAR methodology is provided with a brief examination of the ‘mainstream’ algorithms. In fact, a myriad of 4D-QSAR methods have been implemented and applied practically for a diverse range of molecules. It seems that, 4D-QSAR approach has been experiencing a promising renaissance of interests that might be fuelled by the rising power of the graphics processing unit (GPU) clusters applied to full-atom MD-based simulations of the protein-ligand complexes.

Synthesis and Hybrid SAR Property Modeling of Novel Cholinesterase Inhibitors

A library of novel 4-{[(benzyloxy)carbonyl]amino}-2-hydroxybenzoic acid amides was designed and synthesized in order to provide potential acetyl- and butyrylcholinesterase (AChE/BChE) inhibitors; the in vitro inhibitory profile and selectivity index were specified. Benzyl(3-hydroxy-4-{[2-(trifluoromethoxy)phenyl]carbamoyl}phenyl)carbamate was the best AChE inhibitor with the inhibitory concentration of IC₅₀ = 36.05 µM in the series, while benzyl{3-hydroxy-4-[(2-methoxyphenyl)carbamoyl]phenyl}-carbamate was the most potent BChE inhibitor (IC₅₀ = 22.23 µM) with the highest selectivity for BChE (SI = 2.26). The cytotoxic effect was evaluated in vitro for promising AChE/BChE inhibitors. The newly synthesized adducts were subjected to the quantitative shape comparison with the generation of an averaged pharmacophore pattern. Noticeably, three pairs of fairly similar fluorine/bromine-containing compounds can potentially form the activity cliff that is manifested formally by high structure–activity landscape index (SALI) numerical values. The molecular docking study was conducted for the most potent AChE/BChE inhibitors, indicating that the hydrophobic interactions were overwhelmingly generated with Gln119, Asp70, Pro285, Thr120, and Trp82 aminoacid residues, while the hydrogen bond (HB)-donor ones were dominated with Thr120. π-stacking interactions were specified with the Trp82 aminoacid residue of chain A as well. Finally, the stability of chosen liganded enzymatic systems was assessed using the molecular dynamic simulations. An attempt was made to explain the noted differences of the selectivity index for the most potent molecules, especially those bearing unsubstituted and fluorinated methoxy group.

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.

QSAR/QSPR models based on quantum chemistry for risk assessment of pesticides according to current European legislation

In Europe, agencies and official organizations involved in the pesticide control such as the EFSA, ECHA, JRC and ECETOC or even the OECD are pointing out that the software tools based on quantitative structure relationship models, i.e. QSAR and QSPR, have a huge potential to improve the pesticide risk assessment process. In this sense, these non-animal test methods can promote the competitiveness of agriculture in this region: the consumer safety is increased with them due to the possibility of perform an overall better risk assessment of the degradation products and metabolites from pesticides. However, the use of theses computational-based (in silico) tools must be much more systematised and harmonised, improving their validation and including case studies to test them. To open databases, incorporating critical data in an orderly manner for building the models, becomes also necessary. Moreover, quantum chemistry through the Density Functional Theory should be promoted as tool for calculation of quantum descriptors, especially for the study of similar compounds with the same carbon skeleton but differing substitution patterns, e.g. isomers.

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).

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.

Use of big data in drug development for precision medicine: an update

INTRODUCTION

Big-data-driven drug development resources and methodologies have been evolving with ever-expanding data from large-scale biological experiments, clinical trials, and medical records from participants in data collection initiatives. The enrichment of biological- and clinical-context-specific large-scale data has enabled computational inference more relevant to real-world biomedical research, particularly identification of therapeutic targets and drugs for specific diseases and clinical scenarios.

AREAS COVERED

Here we overview recent progresses made in the fields: new big-data-driven approach to therapeutic target discovery, candidate drug prioritization, inference of clinical toxicity, and machine-learning methods in drug discovery.

EXPERT OPINION

In the near future, much larger volumes and complex datasets for precision medicine will be generated, e.g., individual and longitudinal multi-omic, and direct-to-consumer datasets. Closer collaborations between experts with different backgrounds would also be required to better translate analytic results into prognosis and treatment in the clinical practice. Meanwhile, cloud computing with protected patient privacy would become more routine analytic practice to fill the gaps within data integration along with the advent of big-data. To conclude, integration of multitudes of data generated for each individual along with techniques tailored for big-data analytics may eventually enable us to achieve precision medicine.

Novel Benzene-Based Carbamates for AChE/BChE Inhibition: Synthesis and Ligand/Structure-Oriented SAR Study

A series of new benzene-based derivatives was designed, synthesized and comprehensively characterized. All of the tested compounds were evaluated for their in vitro ability to potentially inhibit the acetyl- and butyrylcholinesterase enzymes. The selectivity index of individual molecules to cholinesterases was also determined. Generally, the inhibitory potency was stronger against butyryl- compared to acetylcholinesterase; however, some of the compounds showed a promising inhibition of both enzymes. In fact, two compounds (23, benzyl ethyl(1-oxo-1-phenylpropan-2-yl)carbamate and 28, benzyl (1-(3-chlorophenyl)-1-oxopropan-2-yl) (methyl)carbamate) had a very high selectivity index, while the second one (28) reached the lowest inhibitory concentration IC₅₀ value, which corresponds quite well with galanthamine. Moreover, comparative receptor-independent and receptor-dependent structure–activity studies were conducted to explain the observed variations in inhibiting the potential of the investigated carbamate series. The principal objective of the ligand-based study was to comparatively analyze the molecular surface to gain insight into the electronic and/or steric factors that govern the ability to inhibit enzyme activities. The spatial distribution of potentially important steric and electrostatic factors was determined using the probability-guided pharmacophore mapping procedure, which is based on the iterative variable elimination method. Additionally, planar and spatial maps of the host–target interactions were created for all of the active compounds and compared with the drug molecules using the docking methodology.

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.

Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery

The therapeutic properties of plants have been recognised since time immemorial. Many pathological conditions have been treated using plant-derived medicines. These medicines are used as concoctions or concentrated plant extracts without isolation of active compounds. Modern medicine however, requires the isolation and purification of one or two active compounds. There are however a lot of global health challenges with diseases such as cancer, degenerative diseases, HIV/AIDS and diabetes, of which modern medicine is struggling to provide cures. Many times the isolation of "active compound" has made the compound ineffective. Drug discovery is a multidimensional problem requiring several parameters of both natural and synthetic compounds such as safety, pharmacokinetics and efficacy to be evaluated during drug candidate selection. The advent of latest technologies that enhance drug design hypotheses such as Artificial Intelligence, the use of 'organ-on chip' and microfluidics technologies, means that automation has become part of drug discovery. This has resulted in increased speed in drug discovery and evaluation of the safety, pharmacokinetics and efficacy of candidate compounds whilst allowing novel ways of drug design and synthesis based on natural compounds. Recent advances in analytical and computational techniques have opened new avenues to process complex natural products and to use their structures to derive new and innovative drugs. Indeed, we are in the era of computational molecular design, as applied to natural products. Predictive computational softwares have contributed to the discovery of molecular targets of natural products and their derivatives. In future the use of quantum computing, computational softwares and databases in modelling molecular interactions and predicting features and parameters needed for drug development, such as pharmacokinetic and pharmacodynamics, will result in few false positive leads in drug development. This review discusses plant-based natural product drug discovery and how innovative technologies play a role in next-generation drug discovery.

Toxicity Classification of Oxide Nanomaterials: Effects of Data Gap Filling and PChem Score-based Screening Approaches

Development of nanotoxicity prediction models is becoming increasingly important in the risk assessment of engineered nanomaterials. However, it has significant obstacles caused by the wide heterogeneities of published literature in terms of data completeness and quality. Here, we performed a meta-analysis of 216 published articles on oxide nanoparticles using 14 attributes of physicochemical, toxicological and quantum-mechanical properties. Particularly, to improve completeness and quality of the extracted dataset, we adapted two preprocessing approaches: data gap-filling and physicochemical property based scoring. Performances of nano-SAR classification models revealed that the dataset with the highest score value resulted in the best predictivity with compromise in its applicability domain. The combination of physicochemical and toxicological attributes was proved to be more relevant to toxicity classification than quantum-mechanical attributes. Overall, by adapting these two preprocessing methods, we demonstrated that meta-analysis of nanotoxicity literatures could provide an effective alternative for the risk assessment of engineered nanomaterials.

Proline-Based Carbamates as Cholinesterase Inhibitors

Series of twenty-five benzyl (2S)-2-(arylcarbamoyl)pyrrolidine-1-carboxylates was prepared and completely characterized. All the compounds were tested for their in vitro ability to inhibit acetylcholinesterase (AChE) and butyrylcholinesterase (BChE), and the selectivity of compounds to individual cholinesterases was determined. Screening of the cytotoxicity of all the compounds was performed using a human monocytic leukaemia THP-1 cell line, and the compounds demonstrated insignificant toxicity. All the compounds showed rather moderate inhibitory effect against AChE; benzyl (2S)-2-[(2-chlorophenyl)carbamoyl]pyrrolidine-1-carboxylate (IC₅₀ = 46.35 μM) was the most potent agent. On the other hand, benzyl (2S)-2-[(4-bromophenyl)-] and benzyl (2S)-2-[(2-bromophenyl)carbamoyl]pyrrolidine-1-carboxylates expressed anti-BChE activity (IC₅₀ = 28.21 and 27.38 μM, respectively) comparable with that of rivastigmine. The ortho-brominated compound as well as benzyl (2S)-2-[(2-hydroxyphenyl)carbamoyl]pyrrolidine-1-carboxylate demonstrated greater selectivity to BChE. The in silico characterization of the structure–inhibitory potency for the set of proline-based carbamates considering electronic, steric and lipophilic properties was provided using comparative molecular surface analysis (CoMSA) and principal component analysis (PCA). Moreover, the systematic space inspection with splitting data into the training/test subset was performed to monitor the statistical estimators performance in the effort to map the probability-guided pharmacophore pattern. The comprehensive screening of the AChE/BChE profile revealed potentially relevant structural and physicochemical features that might be essential for mapping of the carbamates inhibition efficiency indicating qualitative variations exerted on the reaction site by the substituent in the 3′-/4′-position of the phenyl ring. In addition, the investigation was completed by a molecular docking study of recombinant human AChE.

In silico environmental chemical science: properties and processes from statistical and computational modelling

Quantitative structure-activity relationships (QSARs) have long been used in the environmental sciences. More recently, molecular modeling and chemoinformatic methods have become widespread. These methods have the potential to expand and accelerate advances in environmental chemistry because they complement observational and experimental data with "in silico" results and analysis. The opportunities and challenges that arise at the intersection between statistical and theoretical in silico methods are most apparent in the context of properties that determine the environmental fate and effects of chemical contaminants (degradation rate constants, partition coefficients, toxicities, etc.). The main example of this is the calibration of QSARs using descriptor variable data calculated from molecular modeling, which can make QSARs more useful for predicting property data that are unavailable, but also can make them more powerful tools for diagnosis of fate determining pathways and mechanisms. Emerging opportunities for "in silico environmental chemical science" are to move beyond the calculation of specific chemical properties using statistical models and toward more fully in silico models, prediction of transformation pathways and products, incorporation of environmental factors into model predictions, integration of databases and predictive models into more comprehensive and efficient tools for exposure assessment, and extending the applicability of all the above from chemicals to biologicals and materials.

Use of big data in drug development for precision medicine

Drug development has been a costly and lengthy process with an extremely low success rate and lack of consideration of individual diversity in drug response and toxicity. Over the past decade, an alternative "big data" approach has been expanding at an unprecedented pace based on the development of electronic databases of chemical substances, disease gene/protein targets, functional readouts, and clinical information covering inter-individual genetic variations and toxicities. This paradigm shift has enabled systematic, high-throughput, and accelerated identification of novel drugs or repurposed indications of existing drugs for pathogenic molecular aberrations specifically present in each individual patient. The exploding interest from the information technology and direct-to-consumer genetic testing industries has been further facilitating the use of big data to achieve personalized Precision Medicine. Here we overview currently available resources and discuss future prospects.

In silico toxicology: computational methods for the prediction of chemical toxicity

Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models. WIREs Comput Mol Sci 2016, 6:147-172. doi: 10.1002/wcms.1240 For further resources related to this article, please visit the WIREs website.

Multidimensional (3D/4D-QSAR) probability-guided pharmacophore mapping: investigation of activity profile for a series of drug absorption promoters

A hybrid approach that combines 3D and 4D-QSAR methods based on grid and neural paradigms with automated IVE-PLS procedure was examined to identify the pharmacophore pattern for cholic acid derivatives as potential drug absorption promoters.