Virtual ligand screening: strategies, perspectives and limitations

Computational study for suppression of CD25/IL-2 interaction

Cancer recurrence presents a huge challenge in cancer patient management. Immune escape is a key mechanism of cancer progression and metastatic dissemination. CD25 is expressed in regulatory T (Treg) cells including tumor-infiltrating Treg cells (TI-Tregs). These cells specially activate and reinforce immune escape mechanism of cancers. The suppression of CD25/IL-2 interaction would be useful against Treg cells activation and ultimately immune escape of cancer. Here, software, web servers and databases were used, at which in silico designed small interfering RNAs (siRNAs), de novo designed peptides and virtual screened small molecules against CD25 were introduced for the prospect of eliminating cancer immune escape and obtaining successful treatment. We obtained siRNAs with low off-target effects. Further, small molecules based on the binding homology search in ligand and receptor similarity were introduced. Finally, the critical amino acids on CD25 were targeted by a de novo designed peptide with disulfide bond. Hence we introduced computational-based antagonists to lay a foundation for further in vitro and in vivo studies.

Two Methods, One Goal: Structural Differences between Cocrystallization and Crystal Soaking to Discover Ligand Binding Poses

In lead optimization, protein crystallography is an indispensable tool to analyze drug binding. Binding modes and non-covalent interaction inventories are essential to design follow-up synthesis candidates. Two protocols are commonly applied to produce protein-ligand complexes: cocrystallization and soaking. Because of its time and cost effectiveness, soaking is the more popular method. Taking eight ligand hinge binders of protein kinase A, we demonstrate that cocrystallization is superior. Particularly for flexible proteins, such as kinases, and larger ligands cocrystallization captures more reliable the correct binding pose and induced protein adaptations. The geometrical discrepancies between soaking and cocrystallization appear smaller for fragment-sized ligands. For larger flexible ligands that trigger conformational changes of the protein, soaking can be misleading and underestimates the number of possible polar interactions due to inadequate, highly impaired positions of protein amino-acid side and main chain atoms. Thus, if applicable cocrystallization should be the gold standard to study protein-ligand complexes.

Molecular Recognition and Self-Organization in Life Phenomena Studied by a Statistical Mechanics of Molecular Liquids, the RISM/3D-RISM Theory

There are two molecular processes that are essential for living bodies to maintain their life: the molecular recognition, and the self-organization or self-assembly. Binding of a substrate by an enzyme is an example of the molecular recognition, while the protein folding is a good example of the self-organization process. The two processes are further governed by the other two physicochemical processes: solvation and the structural fluctuation. In the present article, the studies concerning the two molecular processes carried out by Hirata and his coworkers, based on the statistical mechanics of molecular liquids or the RISM/3D-RISM theory, are reviewed.

Fragment Binding to the Nsp3 Macrodomain of SARS-CoV-2 Identified Through Crystallographic Screening and Computational Docking

The SARS-CoV-2 macrodomain (Mac1) within the non-structural protein 3 (Nsp3) counteracts host-mediated antiviral ADP-ribosylation signalling. This enzyme is a promising antiviral target because catalytic mutations render viruses non-pathogenic. Here, we report a massive crystallographic screening and computational docking effort, identifying new chemical matter primarily targeting the active site of the macrodomain. Crystallographic screening of diverse fragment libraries resulted in 214 unique macrodomain-binding fragments, out of 2,683 screened. An additional 60 molecules were selected from docking over 20 million fragments, of which 20 were crystallographically confirmed. X-ray data collection to ultra-high resolution and at physiological temperature enabled assessment of the conformational heterogeneity around the active site. Several crystallographic and docking fragment hits were validated for solution binding using three biophysical techniques (DSF, HTRF, ITC). Overall, the 234 fragment structures presented explore a wide range of chemotypes and provide starting points for development of potent SARS-CoV-2 macrodomain inhibitors.

Recent advances in computational methods for biosensor design

Biosensors are analytical tools with a great application in healthcare, food quality control, and environmental monitoring. They are of considerable interest to be designed by using cost-effective and efficient approaches. Designing biosensors with improved functionality or application in new target detection has been converted to a fast-growing field of biomedicine and biotechnology branches. Experimental efforts have led to valuable successes in the field of biosensor design; however, some deficiencies restrict their utilization for this purpose. Computational design of biosensors is introduced as a promising key to eliminate the gap. A set of reliable structure prediction of the biosensor segments, their stability, and accurate descriptors of molecular interactions are required to computationally design biosensors. In this review, we provide a comprehensive insight into the progress of computational methods to guide the design and development of biosensors, including molecular dynamics simulation, quantum mechanics calculations, molecular docking, virtual screening, and a combination of them as the hybrid methodologies. By relying on the recent advances in the computational methods, an opportunity emerged for them to be complementary or an alternative to the experimental methods in the field of biosensor design.

Evaluation of Artificial Intelligence in Participating Structure-Based Virtual Screening for Identifying Novel Interleukin-1 Receptor Associated Kinase-1 Inhibitors

Interleukin-1 receptor associated kinase-1 (IRAK1) exhibits important roles in inflammation, infection, and autoimmune diseases; however, only a few inhibitors have been discovered. In this study, at first, a discriminatory structure-based virtual screening (SBVS) was employed, but only one active compound (compound 1, IC₅₀ = 2.25 μM) was identified. The low hit rate (2.63%) which derives from the weak discriminatory power of docking among high-scored molecules was observed in our virtual screening (VS) process for IRAK1 inhibitor. Furthermore, an artificial intelligence (AI) method, which employed a support vector machine (SVM) model, integrated information of molecular docking, pharmacophore scoring and molecular descriptors was constructed to enhance the traditional IRAK1-VS protocol. Using AI, it was found that VS of IRAK1 inhibitors excluded by over 50% of the inactive compounds, which could significantly improve the prediction accuracy of the SBVS model. Moreover, four active molecules (two of which exhibited comparative IC₅₀ with compound 1) were accurately identified from a set of highly similar candidates. Amongst, compounds with better activity exhibited good selectivity against IRAK4. The AI assisted workflow could serve as an effective tool for enhancement of SBVS.

Deciphering inhibitory activity of flavonoids against tau protein kinases: a coupled molecular docking and quantum chemical study

Today, Alzheimer's disease (AD) is one of the most important neurodegenerative disorders that affected millions of people worldwide. Hundreds of academic investigations highlighted the potential roles of natural metabolites in the cornerstone of AD prevention. Nevertheless, alkaloids are only metabolites that successfully showed promising clinical therapeutic effects on the prevention of AD. In this regard, other plant metabolites such as flavonoids are also considered as promising substances in the improvement of AD complications. The lack of data on molecular mode of action of flavonoids inside brain tissues, and their potential to transport across the blood-brain barrier, a physical hindrance between bloodstream and brain tissues, limited the large-scale application of these compounds for AD therapy programs. Herein, a coupled docking and quantum study was applied to determine the binding mode of flavonoids and three protein kinases involved in the pathogenesis of AD. The results suggested that all docked metabolites showed considerable binding affinity to interact with target receptors, but some compounds possessed higher binding energy values. Because docking simulation cannot entirely reveal the potential roles of ligand substructures in the interaction with target residues, quantum chemical analyses (QCAs) were performed to cover this drawback. Accordingly, QCAs determined that distribution of molecular orbitals have a pivotal function in the determination of the type of reaction between ligands and receptors; therefore, using such quantum chemical descriptors may correct the results of virtual docking outcomes to highlight promising backbones for further developments.Communicated by Ramaswamy H. Sarma.

Current Approaches to Drug Discovery for Chagas Disease: Methodological Advances

BACKGROUND

In recent years, there has been an improvement in the in vitro and in vivo methodology for the screening of anti-chagasic compounds. Millions of compounds can now have their activity evaluated (in large compound libraries) by means of high throughput in vitro screening assays.

OBJECTIVE

Current approaches to drug discovery for Chagas disease.

METHOD

This review article examines the contribution of these methodological advances in medicinal chemistry in the last four years, focusing on Trypanosoma cruzi infection, obtained from the PubMed, Web of Science, and Scopus databases.

RESULTS

Here, we have shown that the promise is increasing each year for more lead compounds for the development of a new drug against Chagas disease.

CONCLUSION

There is increased optimism among those working with the objective to find new drug candidates for optimal treatments against Chagas disease.

Chemical Screening of Nuclear Receptor Modulators

Nuclear receptors are ligand-inducible transcriptional factors that control multiple biological phenomena, including proliferation, differentiation, reproduction, metabolism, and the maintenance of homeostasis. Members of the nuclear receptor superfamily have marked structural and functional similarities, and their domain functionalities and regulatory mechanisms have been well studied. Various modulators of nuclear receptors, including agonists and antagonists, have been developed as tools for elucidating nuclear receptor functions and also as drug candidates or lead compounds. Many assay systems are currently available to evaluate the modulation of nuclear receptor functions, and are useful as screening tools in the discovery and development of new modulators. In this review, we cover the chemical screening methods for nuclear receptor modulators, focusing on assay methods and chemical libraries for screening. We include some recent examples of the discovery of nuclear receptor modulators.

SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design

Quantum mechanical (QM) methods have been gaining importance in structure-based drug design where a reliable description of protein-ligand interactions is of utmost significance. However, strategies i. e. QM/MM, fragmentation or semiempirical (SQM) methods had to be pursued to overcome the unfavorable scaling of QM methods. Various SQM-based approaches have significantly contributed to the accuracy of docking and improvement of lead compounds. Parametrizations of SQM and implicit solvent methods in our laboratory have been instrumental to obtain a reliable SQM-based scoring function. The experience gained in its application for activity ranking of ligands binding to tens of protein targets resulted in setting up a faster SQM/COSMO scoring approach, which outperforms standard scoring methods in native pose identification for two dozen protein targets with ten thousand poses. Recently, SQM/COSMO was effectively applied in a proof-of-concept study of enrichment in virtual screening. Due to its superior performance, feasibility and chemical generality, we propose the SQM/COSMO approach as an efficient tool in structure-based drug design.

Exploring different approaches to improve the success of drug discovery and development projects: a review

Background

There has been a significant increase in the cost and timeline of delivering new drugs for clinical use over the last three decades. Despite the increased investments in research infrastructure by pharmaceutical companies and technological advances in the scientific tools available, efforts to increase the number of molecules coming through the drug development pipeline have largely been unfruitful.

Main body

A non-systematic review of the current literature was undertaken to enumerate the various strategies employed to improve the success rates in the pharmaceutical research and development. The review covers the exploitation of genomics and proteomics, complementarity of target-based and phenotypic efficacy screening platforms, drug repurposing and repositioning, collaborative research, focusing on underserved therapeutic fields, outsourcing strategy, and pharmaceutical modeling and artificial intelligence. Examples of successful drug discoveries achieved through application of these strategies are highlighted and discussed herein.

Conclusions

Genomics and proteomics have uncovered a wide array of potential drug targets and are facilitative of enhanced scrupulous target identification and validation thus reducing efficacy-related drug attrition. When used complementarily, phenotypic and target-based screening platforms would likely allow serendipitous drug discovery while increasing rationality in drug design. Drug repurposing and repositioning reduces financial risks in drug development accompanied by cost and time savings, while prolonging patent exclusivity hence increased returns on investment to the innovator company. Equally important, collaborative research is facilitative of cross-fertilization and refinement of ideas, while sharing resources and expertise, hence reducing overhead costs in the early stages of drug discovery. Underserved therapeutic fields are niche drug discovery areas that may be used to experiment and launch novel drug targets, while exploiting incentivized benefits afforded by drug regulatory authorities. Outsourcing allows the pharma industries to focus on their core competencies while deriving greater efficiency of specialist contract research organizations. The existing and emerging pharmaceutical modeling and artificial intelligence softwares and tools allow for in silico computation enabling more efficient computer-aided drug design. Careful selection and application of these strategies, singly or in combination, may potentially harness pharmaceutical research and innovation.

Toward Simple, Predictive Understanding of Protein-Ligand Interactions: Electronic Structure Calculations on Torpedo Californica Acetylcholinesterase Join Forces with the Chemist's Intuition

Contemporary efforts for empirically-unbiased modeling of protein-ligand interactions entail a painful tradeoff - as reliable information on both noncovalent binding factors and the dynamic behavior of a protein-ligand complex is often beyond practical limits. We demonstrate that information drawn exclusively from static molecular structures can be used for reproducing and predicting experimentally-measured binding affinities for protein-ligand complexes. In particular, inhibition constants (Ki) were calculated for seven different competitive inhibitors of Torpedo californica acetylcholinesterase using a multiple-linear-regression-based model. The latter, incorporating five independent variables - drawn from QM cluster, DLPNO-CCSD(T) calculations and LED analyses on the seven complexes, each containing active amino-acid residues found within interacting distance (3.5 Å) from the corresponding ligand - is shown to recover 99.9% of the sum of squares for measured Ki values, while having no statistically-significant residual errors. Despite being fitted to a small number of data points, leave-one-out cross-validation statistics suggest that it possesses surprising predictive value (Q2LOO=0.78, or 0.91 upon removal of a single outlier). This thus challenges ligand-invariant definitions of active sites, such as implied in the lock-key binding theory, as well as in alternatives highlighting shape-complementarity without taking electronic effects into account. Broader implications of the current work are discussed in dedicated appendices.

Augmenting Hit Identification by Virtual Screening Techniques in Small Molecule Drug Discovery

Two orthogonal approaches for hit identification in drug discovery are large-scale in vitro and in silico screening. In recent years, due to the emergence of new targets and a rapid increase in the size of the readily synthesizable chemical space, there is a growing emphasis on the integration of the two techniques to improve the hit finding efficiency. Here, we highlight three examples of drug discovery projects at Merck & Co., Inc., Kenilworth, NJ, USA in which different virtual screening (VS) techniques, each specifically tailored to leverage knowledge available for the target, were utilized to augment the selection of high-quality chemical matter for in vitro assays and to enhance the diversity and tractability of hits. Central to success is a fully integrated workflow combining in silico and experimental expertise at every stage of the hit identification process. We advocate that workflows encompassing VS as part of an integrated hit finding plan should be widely adopted to accelerate hit identification and foster cross-functional collaborations in modern drug discovery.

Pharmacophore modeling of JAK1: A target infested with activity-cliffs

Janus kinase 1 (JAK1) is protein kinase involved in autoimmune diseases (AIDs). JAK1 inhibitors have shown promising results in treating AIDs. JAK1 inhibitors are known to exhibit regions of SAR discontinuity or activity cliffs (ACs). ACs represent fundamental challenge to successful QSAR/pharmacophore modeling because QSAR modeling rely on the basic premise that activity is a smooth continuous function of structure. We propose that ACs exist because active ACs members exhibit subtle, albeit critical, enthalpic features absent from their inactive twins. In this context we compared the performances of two computational modeling workflows in extracting valid pharmacophores from 151 diverse JAK1 inhibitors that include ACs: QSAR-guided pharmacophore selection versus docking-based comparative intermolecular contacts analysis (db-CICA). The two methods were judged based on the receiver operating characteristic (ROC) curves of their corresponding pharmacophore models and their abilities to distinguish active members among established JAK1 ACs. db-CICA modeling significantly outperformed ligand-based pharmacophore modeling. The resulting optimal db-CICA pharmacophore was used as virtual search query to scan the National Cancer Institute (NCI) database for novel JAK1 inhibitory leads. The most active hit showed IC₅₀ of 1.04 μM. This study proposes the use of db-CICA modeling as means to extract valid pharmacophores from SAR data infested with ACs.

Covalent Complex of DNA and Bacterial Topoisomerase: Implications in Antibacterial Drug Development

A topoisomerase-DNA transient covalent complex can be a druggable target for novel topoisomerase poison inhibitors that represent a new class of antibacterial or anticancer drugs. Herein, we have investigated molecular features of the functionally important Escherichia coli topoisomerase I (EctopoI)-DNA covalent complex (EctopoIcc) for molecular simulations, which is very useful in the development of new antibacterial drugs. To demonstrate the usefulness of our approach, we used a model small molecule (SM), NSC76027, obtained from virtual screening. We examined the direct binding of NSC76027 to EctopoI as well as inhibition of EctopoI relaxation activity of this SM via experimental techniques. We then performed molecular dynamics (MD) simulations to investigate the dynamics and stability of EctopoIcc and EctopoI-NSC76027-DNA ternary complex. Our simulation results show that NSC76027 forms a stable ternary complex with EctopoIcc. EctopoI investigated here also serves as a model system for investigating a complex of topoisomerase and DNA in which DNA is covalently attached to the protein.

Combining Virtual Screening Protocol and In Vitro Evaluation towards the Discovery of BACE1 Inhibitors

The treatment options for a patient diagnosed with Alzheimer’s disease (AD) are currently limited. The cerebral accumulation of amyloid-β (Aβ) is a critical molecular event in the pathogenesis of AD. When the amyloidogenic β-secretase (BACE1) is inhibited, the production of Aβ peptide is reduced. Henceforth, the main goal of this study is the discovery of new small bioactive molecules that potentially reach the brain and inhibit BACE1. The work was conducted by a customized molecular modelling protocol, including pharmacophore-based and molecular docking-based virtual screening (VS). Structure-based (SB) and ligand-based (LB) pharmacophore models were designed to accurately screen several drug-like compound databases. The retrieved hits were subjected to molecular docking and in silico filtered to predict their ability to cross the blood–brain barrier (BBB). Additionally, 34 high-scoring compounds structurally distinct from known BACE1 inhibitors were selected for in vitro screening assay, which resulted in 13 novel hit-compounds for this relevant therapeutic target. This study disclosed new BACE1 inhibitors, proving the utility of combining computational and in vitro approaches for effectively predicting anti-BACE1 agents in the early drug discovery process.

Lapatinib, Nilotinib and Lomitapide Inhibit Haemozoin Formation in Malaria Parasites

With the continued loss of antimalarials to resistance, drug repositioning may have a role in maximising efficiency and accelerating the discovery of new antimalarial drugs. Bayesian statistics was previously used as a tool to virtually screen USFDA approved drugs for predicted β-haematin (synthetic haemozoin) inhibition and in vitro antimalarial activity. Here, we report the experimental evaluation of nine of the highest ranked drugs, confirming the accuracy of the model by showing an overall 93% hit rate. Lapatinib, nilotinib, and lomitapide showed the best activity for inhibition of β-haematin formation and parasite growth and were found to inhibit haemozoin formation in the parasite, providing mechanistic insights into their mode of antimalarial action. We then screened the USFDA approved drugs for binding to the β-haematin crystal, applying a docking method in order to evaluate its performance. The docking method correctly identified imatinib, lapatinib, nilotinib, and lomitapide. Experimental evaluation of 22 of the highest ranked purchasable drugs showed a 24% hit rate. Lapatinib and nilotinib were chosen as templates for shape and electrostatic similarity screening for lead hopping using the in-stock ChemDiv compound catalogue. The actives were novel structures worthy of future investigation. This study presents a comparison of different in silico methods to identify new haemozoin-inhibiting chemotherapeutic alternatives for malaria that proved to be useful in different ways when taking into consideration their strengths and limitations.

Validation of ligands targeting metacaspase-2 (MCA2) from Trypanosoma brucei brucei and their application to MCA5 from T. congolense as possible trypanocides

Metacaspases (MCAs) are ideal drug and diagnostic targets for animal and human African trypanosomiasis, as these cysteine peptidases are absent from the metazoan kingdom and have been implicated in the parasite cell cycle and cell death. Tsetse fly-transmitted trypanosomes that live free in the bloodstream and/or cerebrospinal fluid of the mammalian host cause animal and human African trypanosomiasis (nagana or sleeping sickness respectively). Chemotherapy and chemoprophylaxis are the main forms of control, but in contrast to human trypanocides, the veterinary drugs are old and drug resistance is on the increase. A peptidomimetic library targeting the MCA2 from Trypanosoma brucei brucei has ligands with low IC₅₀ values, some of which were antiparasitic. This study validates the inhibitory activity of these ligands using the protein structure solved by X-ray diffraction after the ligand library was published. Water molecules were shown to be important in substrate binding and strategies to improve the efficacy of these ligands are highlighted. These ligands appear to be pan-specific as they were docked into the active site of the homology modelled MCA5 of animal infective Trypanosoma congolense with similar binding energies and conformations.

In Silico Predictions of Endocrine Disruptors Properties

Endocrine-disrupting chemicals (EDCs) are a broad class of molecules present in our environment that are suspected to cause adverse effects in the endocrine system by interfering with the synthesis, transport, degradation, or action of endogenous ligands. The characterization of the harmful interaction between environmental compounds and their potential cellular targets and the development of robust in vivo, in vitro, and in silico screening methods are important for assessment of the toxic potential of large numbers of chemicals. In this context, computer-aided technologies that will allow for activity prediction of endocrine disruptors and environmental risk assessments are being developed. These technologies must be able to cope with diverse data and connect chemistry at the atomic level with the biological activity at the cellular, organ, and organism levels. Quantitative structure-activity relationship methods became popular for toxicity issues. They correlate the chemical structure of compounds with biological activity through a number of molecular descriptors (e.g., molecular weight and parameters to account for hydrophobicity, topology, or electronic properties). Chemical structure analysis is a first step; however, modeling intermolecular interactions and cellular behavior will also be essential. The increasing number of three-dimensional crystal structures of EDCs' targets has provided a wealth of structural information that can be used to predict their interactions with EDCs using docking and scoring procedures. In the present review, we have described the various computer-assisted approaches that use ligands and targets properties to predict endocrine disruptor activities.

Perspectives towards antiviral drug discovery against Ebola virus

Ebola virus disease (EVD), caused by Ebola viruses, resulted in more than 11 500 deaths according to a recent 2018 WHO report. With mortality rates up to 90%, it is nowadays one of the most deadly infectious diseases. However, no Food and Drug Administration‐approved Ebola drugs or vaccines are available yet with the mainstay of therapy being supportive care. The high fatality rate and absence of effective treatment or vaccination make Ebola virus a category‐A biothreat pathogen. Fortunately, a series of investigational countermeasures have been developed to control and prevent this global threat. This review summarizes the recent therapeutic advances and ongoing research progress from research and development to clinical trials in the development of small‐molecule antiviral drugs, small‐interference RNA molecules, phosphorodiamidate morpholino oligomers, full‐length monoclonal antibodies, and vaccines. Moreover, difficulties are highlighted in the search for effective countermeasures against EVD with additional focus on the interplay between available in silico prediction methods and their evidenced potential in antiviral drug discovery.

Exploring Novel N-Myristoyltransferase Inhibitors: A Molecular Dynamics Simulation Approach

N-Myristoyltransferase (NMT) is a cytosolic monomeric enzyme involved in the allocation of the myristoyl group to the aminoterminal of glycine in several viral and eukaryotic cellular proteins. NMT has been validated as a potential drug target against kinetoplastid for parasitic protozoa. A multistep virtual screening protocol based on the pharmacophore modeling, molecular docking, and molecular dynamics simulation was carried out. Initially, Maybridge database was virtually screened via a validated pharmacophore model. The effective pharmacophore models were accompanied with exclusion volumes to improve their receiver operating characteristic curve to identify potential NMT inhibitors. The hits identified as actives based on the 3D-pharmacophore model were evaluated by molecular docking studies. In stepwise screening, six compounds were shortlisted for the dynamic simulation to get insights into their binding mode. In conclusion, this study provides fundamental information about the architecture of the binding site and some crucial residues that may provide insights into the development of new antiparasitic agents.

Combined Virtual Screening, DFT Calculations and Molecular Dynamics Simulations to Discovery of Potent MMP-9 Inhibitors

Background:

Matrix metalloproteinase-9 (MMP-9) plays a crucial role in the development and progression of cancer. Therefore, identifying its inhibitors has enjoyed numerous attentions. In this report, a hybrid approach, including pharmacophore-based virtual screening, docking studies, and density functional theory (DFT) binding energy calculations followed by molecular dynamics simulations, was used to identify potential MMP-9 inhibitors.

Methods:

Pharmacophore modeling based on ARP101, as a known MMP-9 inhibitor, was performed and followed by virtual screening of ZINC database and docking studies to introduce a set of new ligands as candidates for potent inhibitors of MMP-9. The binding energies of MMP-9 and the selected ligands as well as ARP101, were estimated via the DFT energy calculations. Subsequently, molecular dynamics simulations were applied to evaluate and compare the behavior of ARP101 and the selected ligand in a dynamic environment.

Results:

(S,Z)-6-(((2,3-dihydro-1H-benzo[d]imidazol-2-yl)thio)methylene)-2-((4,6,7- trimethylquinazolin- 2-yl)amino)-1,4,5,6-tetrahydropyrimidin-4-ol, ZINC63611396, with the largest DFT binding energy, was selected as a proper potent MMP-9 inhibitor. Molecular dynamics simulations indicated that the new ligand was stable in the active site.

Conclusion:

The results of this study revealed that compared to the binding energies achieved from the docking studies, the binding energies obtained from the DFT calculations were more consistent with the intermolecular interactions. Also, the interaction between the Zinc ion and ligand, in particular the Zn2+-ligand distance, played a profound role in the quantity of DFT binding energies.

Bioassay Directed Isolation, Biological Evaluation and in Silico Studies of New Isolates from Pteris cretica L

Members of genus Pteris have their established role in the traditional herbal medicine system. In the pursuit to identify its biologically active constituents, the specie Pteris cretica L. (P. cretica) was selected for the bioassay-guided isolation. Two new maleates (F9 and CB18) were identified from the chloroform extract and the structures of the isolates were elucidated through their spectroscopic data. The putative targets, that potentially interact with both of these isolates, were identified through reverse docking by using in silico tools PharmMapper and ReverseScreen3D. On the basis of reverse docking results, both isolates were screened for their antioxidant, acetylcholinesterase (AChE) inhibition, α-glucosidase (GluE) inhibition and antibacterial activities. Both isolates depicted moderate potential for the selected activities. Furthermore, docking studies of both isolates were also studied to investigate the binding mode with respective targets followed by molecular dynamics simulations and binding free energies. Thereby, the current study embodies the poly-pharmacological potential of P. cretica.

Molecular Docking Guided Grid-Independent Descriptor Analysis to Probe the Impact of Water Molecules on Conformational Changes of hERG Inhibitors in Drug Trapping Phenomenon

Human ether a-go-go related gene (hERG) or KV11.1 potassium channels mediate the rapid delayed rectifier current (I_Kr) in cardiac myocytes. Drug-induced inhibition of hERG channels has been implicated in the development of acquired long QT syndrome type (aLQTS) and fatal arrhythmias. Several marketed drugs have been withdrawn for this reason. Therefore, there is considerable interest in developing better tests for predicting drugs which can block the hERG channel. The drug-binding pocket in hERG channels, which lies below the selectivity filter, normally contains K⁺ ions and water molecules. In this study, we test the hypothesis that these water molecules impact drug binding to hERG. We developed 3D QSAR models based on alignment independent descriptors (GRIND) using docked ligands in open and closed conformations of hERG in the presence (solvated) and absence (non-solvated) of water molecules. The ligand–protein interaction fingerprints (PLIF) scheme was used to summarize and compare the interactions. All models delineated similar 3D hERG binding features, however, small deviations of about ~0.4 Å were observed between important hotspots of molecular interaction fields (MIFs) between solvated and non-solvated hERG models. These small changes in conformations do not affect the performance and predictive power of the model to any significant extent. The model that exhibits the best statistical values was attained with a cryo_EM structure of the hERG channel in open state without water. This model also showed the best R² of 0.58 and 0.51 for the internal and external validation test sets respectively. Our results suggest that the inclusion of water molecules during the docking process has little effect on conformations and this conformational change does not impact the predictive ability of the 3D QSAR models.

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

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

Rescoring Virtual Screening Results with the MM-PBSA Methods: Beware of Internal Dielectric Constants

With the potential of improving virtual screening outcome, MM-PB/GBSA has become a disputed method that requires extensive testing and tuning to provide the optimal results. One of the tuning factors is the internal or solute dielectric constant. We have applied three test sets with receptors of different categories and libraries from different sources to investigate the underlying issue related to this constant. We discovered that increasing internal dielectric value does not improve the virtual screening enrichment qualitatively. More interestingly, nonpolar and polar calculated energies act differently in libraries with different molecular weight distributions. From this work, the performance of MM-PBSA rescoring in virtual screening is more library- than receptor-dependent.

In Silico and 3D QSAR Studies of Natural Based Derivatives as Xanthine Oxidase Inhibitors

BACKGROUND

A large number of disorders and their symptoms emerge from deficiency or overproduction of specific metabolites has drawn the attention for the discovery of new therapeutic agents for the treatment of disorders. Various approaches such as computational drug design have provided the new methodology for the selection and evaluation of target protein and the lead compound mechanistically. For instance, the overproduction of xanthine oxidase causes the accumulation of uric acid which can prompt gout.

OBJECTIVE

In the present study we critically discussed the various techniques such as 3-D QSAR and molecular docking for the study of the natural based xanthine oxidase inhibitors with their mechanistic insight into the interaction of xanthine oxidase and various natural leads.

CONCLUSION

The computational studies of deferent natural compounds were discussed as a result the flavonoids, anthraquinones, xanthones shown the remarkable inhibitory potential for xanthine oxidase inhibition moreover the flavonoids such as hesperidin and rutin were found as promising candidates for further exploration.

Middle-way flexible docking: Pose prediction using mixed-resolution Monte Carlo in estrogen receptor α

There is a vast gulf between the two primary strategies for simulating protein-ligand interactions. Docking methods significantly limit or eliminate protein flexibility to gain great speed at the price of uncontrolled inaccuracy, whereas fully flexible atomistic molecular dynamics simulations are expensive and often suffer from limited sampling. We have developed a flexible docking approach geared especially for highly flexible or poorly resolved targets based on mixed-resolution Monte Carlo (MRMC), which is intended to offer a balance among speed, protein flexibility, and sampling power. The binding region of the protein is treated with a standard atomistic force field, while the remainder of the protein is modeled at the residue level with a Gō model that permits protein flexibility while saving computational cost. Implicit solvation is used. Here we assess three facets of the MRMC approach with implications for other docking studies: (i) the role of receptor flexibility in cross-docking pose prediction; (ii) the use of non-equilibrium candidate Monte Carlo (NCMC) and (iii) the use of pose-clustering in scoring. We examine 61 co-crystallized ligands of estrogen receptor α, an important cancer target known for its flexibility. We also compare the performance of the MRMC approach with Autodock smina. Adding protein flexibility, not surprisingly, leads to significantly lower total energies and stronger interactions between protein and ligand, but notably we document the important role of backbone flexibility in the improvement. The improved backbone flexibility also leads to improved performance relative to smina. Somewhat unexpectedly, our implementation of NCMC leads to only modestly improved sampling of ligand poses. Overall, the addition of protein flexibility improves the performance of docking, as measured by energy-ranked poses, but we do not find significant improvements based on cluster information or the use of NCMC. We discuss possible improvements for the model including alternative coarse-grained force fields, improvements to the treatment of solvation, and adding additional types of NCMC moves.

Hierarchical Virtual Screening of Potential Insectides Inhibitors of Acetylcholinesterase and Juvenile Hormone from Temephos

Aedes aegypti (Linnaeus, 1762; Diptera: Culicidae) is the main vector transmitting viral diseases such as dengue fever, dengue haemorrhagic fever, urban yellow fever, zika and chikungunya. Worldwide, especially in the Americas and Brazil, many cases of dengue have been reported in recent years, which have shown significant growth. The main control strategy is the elimination of the vector, carried out through various education programs, to change human habits, but the most usual is biological control, together with environmental management and chemical control. The most commonly insecticide used is temephos (an organophosphorus compound), but Aedes aegypti populations have shown resistance and the product is highly toxic, so we chose it as a template molecule to perform a ligand-based virtual screening in the ChemBrigde (DIVERSet-CL subcollection) database, searching for derivatives with similarity in shape (ROCS) and electrostatic potential (EON). Thus, fourty-five molecules were filtered based on their pharmacokinetic and toxicological properties and 11 molecules were selected by a molecular docking study, including binding affinity and mode of interaction. The L46, L66 and L68 molecules show potential inhibitory activity for both the insect (−9.28, −10.08 and −6.78 Kcal/mol, respectively) and human (−6.05, 6.25 and 7.2 Kcal/mol respectively) enzymes, as well as the juvenile hormone protein (−9.2; −10.96 and −8.16 kcal/mol, respectively), showing a significant difference in comparison to the template molecule temephos. Molecules L46, L66 and L68 interacted with important amino acids at each catalytic site of the enzyme reported in the literature. Thus, the molecules here investigated are potential inhibitors for both the acetylcholinesterase enzymes and juvenile hormone protein–from insect and humans, characterizing them as a potential insecticide against the Aedes aegypti mosquito.

Virtual Screening Meets Deep Learning

BACKGROUND

Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence.

OBJECTIVE

This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Impact of ∼omics in the detection and validation of potential anti-infective drugs

New anti-infective drugs are an unmet necessity of modern medicine. The use of ∼omics technologies has exponentially increased the knowledge on active anti-infective structures, where to search for them and their mechanisms of action. Research involving extreme and unique environments (such as endophytes) revealed their potential for many yet unknown active molecules. This work intends to review a recent research involving discovery of secondary metabolites with an established anti-infective action which was mediated by one of the ∼omics sciences: genomics, proteomics, transcriptomics, metabolomics, glycomics or their combinations, as well as the software at the base of these discoveries.

Ligand biological activity predicted by cleaning positive and negative chemical correlations

Predicting ligand biological activity is a key challenge in drug discovery. Ligand-based statistical approaches are often hampered by noise due to undersampling: The number of molecules known to be active or inactive is vastly less than the number of possible chemical features that might determine binding. We derive a statistical framework inspired by random matrix theory and combine the framework with high-quality negative data to discover important chemical differences between active and inactive molecules by disentangling undersampling noise. Our model outperforms standard benchmarks when tested against a set of challenging retrospective tests. We prospectively apply our model to the human muscarinic acetylcholine receptor M1, finding four experimentally confirmed agonists that are chemically dissimilar to all known ligands. The hit rate of our model is significantly higher than the state of the art. Our model can be interpreted and visualized to offer chemical insights about the molecular motifs that are synergistic or antagonistic to M1 agonism, which we have prospectively experimentally verified.

In Silico Approaches for TRP Channel Modulation

The implication of several TRP ion channels (e.g., TRPV1) in diverse physiological and pathological processes has signaled them as pivotal drug targets. Consequently, the identification of selective and potent ligands for these channels is of great interest in pharmacology and biomedicine. However, a major challenge in the design of modulators is ensuring the specificity for their intended targets. In recent years, the emergence of high-resolution structures of ion channels facilitates the computer-assisted drug design at molecular levels. Here we describe some computational methods and general protocols to discover channel modulators, including homology modelling, docking and virtual screening, and structure-based peptide design.

Drug Discovery

Modern chemistry foundations were made in between the 18th and 19th centuries and have been extended in 20th century. R&D towards synthetic chemistry was introduced during the 1960s. Development of new molecular drugs from the herbal plants to synthetic chemistry is the fundamental scientific improvement. About 10-14 years are needed to develop a new molecule with an average cost of more than $800 million. Pharmaceutical industries spend the highest percentage of revenues, but the achievement of desired molecular entities into the market is not increasing proportionately. As a result, an approximate of 0.01% of new molecular entities are approved by the FDA. The highest failure rate is due to inadequate efficacy exhibited in Phase II of the drug discovery and development stage. Innovative technologies such as combinatorial chemistry, DNA sequencing, high-throughput screening, bioinformatics, computational drug design, and computer modeling are now utilized in the drug discovery. These technologies can accelerate the success rates in introducing new molecular entities into the market.

Energy windows for computed compound conformers: covering artefacts or truly large reorganization energies?

The generation of 3D conformers of small molecules underpins most computational drug discovery. Thus, the conformer quality is critical and depends on their energetics. A key parameter is the empirical conformational energy window (ΔEw), since only conformers within ΔEw are retained. However, ΔEw values in use appear unrealistically large. We analyze the factors pertaining to the conformer energetics and ΔEw. We argue that more attention must be focused on the problem of collapsed low-energy conformers. That is due to artificial intramolecular stabilization and occurs even with continuum solvation. Consequently, the conformational energy of extended bioactive structures is artefactually increased, which inflates ΔEw. Thus, this Perspective highlights the issues arising from low-energy conformers and suggests improvements via empirical or physics-based strategies.

Inverse Molecular Docking as a Novel Approach to Study Anticarcinogenic and Anti-Neuroinflammatory Effects of Curcumin

Research efforts are placing an ever increasing emphasis on identifying signal transduction pathways related to the chemopreventive activity of curcumin. Its anticarcinogenic effects are presumably mediated by the regulation of signaling cascades, including nuclear factor κB (NF-κB), activator protein 1 (AP-1), and mitogen-activated protein kinases (MAPK). By modulating signal transduction pathways, curcumin induces apoptosis in malignant cells, thus inhibiting cancer development and progression. Due to the lack of mechanistic insight in the scientific literature, we developed a novel inverse molecular docking protocol based on the CANDOCK algorithm. For the first time, we performed inverse molecular docking of curcumin into a collection of 13,553 available human protein structures from the Protein Data Bank resulting in prioritized target proteins of curcumin. Our predictions were in agreement with the scientific literature and confirmed that curcumin binds to folate receptor β, DNA (cytosine-5)-methyltransferase 3A, metalloproteinase-2, mitogen-activated protein kinase 9, epidermal growth factor receptor and apoptosis-inducing factor 1. We also identified new potential protein targets of curcumin, namely deoxycytidine kinase, NAD-dependent protein deacetylase sirtuin-1 and -2, ecto-5'-nucleotidase, core histone macro-H2A.1, tyrosine-protein phosphatase non-receptor type 11, macrophage colony-stimulating factor 1 receptor, GTPase HRas, aflatoxin B1 aldehyde reductase member 3, aldo-keto reductase family 1 member C3, amiloride-sensitive amine oxidase, death-associated protein kinase 2 and tryptophan-tRNA ligase, that may all play a crucial role in its observed anticancer effects. Moreover, our inverse docking results showed that curcumin potentially binds also to the proteins cAMP-specific 3',5'-cyclic phosphodiesterase 4D and 17-β-hydroxysteroid dehydrogenase type 10, which provides a new explanation for its efficiency in the treatment of Alzheimer's disease. We firmly believe that our computational results will complement and direct future experimental studies on curcumin's anticancer activity as well as on its therapeutic effects against Alzheimer's disease.

Predicting drug-target interactions using Lasso with random forest based on evolutionary information and chemical structure

The identification of drug-target interactions has great significance for pharmaceutical scientific research. Since traditional experimental methods identifying drug-target interactions is costly and time-consuming, the use of machine learning methods to predict potential drug-target interactions has attracted widespread attention. This paper presents a novel drug-target interactions prediction method called LRF-DTIs. Firstly, the pseudo-position specific scoring matrix (PsePSSM) and FP2 molecular fingerprinting were used to extract the features of drug-target. Secondly, using Lasso to reduce the dimension of the extracted feature information and then the Synthetic Minority Oversampling Technique (SMOTE) method was used to deal with unbalanced data. Finally, the processed feature vectors were input into a random forest (RF) classifier to predict drug-target interactions. Through 10 trials of 5-fold cross-validation, the overall prediction accuracies on the enzyme, ion channel (IC), G-protein-coupled receptor (GPCR) and nuclear receptor (NR) datasets reached 98.09%, 97.32%, 95.69%, and 94.88%, respectively, and compared with other prediction methods. In addition, we have tested and verified that our method not only could be applied to predict the new interactions but also could obtain a satisfactory result on the new dataset. All the experimental results indicate that our method can significantly improve the prediction accuracy of drug-target interactions and play a vital role in the new drug research and target protein development. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/LRF-DTIs/ for academic use.

Computer Aided Drug Design and its Application to the Development of Potential Drugs for Neurodegenerative Disorders

Background

Neurodegenerative disorders (NDs) are diverse group of disorders characterized by escalating loss of neurons (structural and functional). The development of potential therapeutics for NDs presents an important challenge, as traditional treatments are inefficient and usually are unable to stop or retard the process of neurodegeneration. Computer-Aided Drug Design (CADD) has emerged as an efficient means of developing candidate drugs for the treatment of many disease types. Applications of CADD approach to drug discovery are progressing day by day. The recent tendency in drug design is to rationally design potent therapeutics with multi-targeting effects, higher efficacies, and fewer side effects, especially in terms of toxicity.

Methods

A wide literature search was performed for writing this review. An updated view on different types of NDs, their effect on human population and a brief introduction to CADD, various approaches involved in this technique, ranging from structural-based to ligand-based drug design has been discussed. The successful application of CADD approaches for the treatment of neurodegenerative disorders is also included in this review.

Results

In this review, we have briefly described about CADD and its use in the development of the therapeutic drug candidates against NDs. The successful applications, limitations and future prospects of this approach have also been discussed.

Conclusion

CADD can assist researchers studying interactions between drugs and receptors. We believe this review will be helpful for better understanding of CADD and its applications towards the discovery of new drug candidates against various fatal NDs.

Interaction of metals from group 10 (Ni, Pd, and Pt) and 11 (Cu, Ag, and Au) with human blood δ-ALA-D: in vitro and in silico studies

Mammalian δ-aminolevulinate dehydratase (δ-ALA-D) is a metalloenzyme, which requires Zn(II) and reduced thiol groups for catalytic activity, and is an important molecular target for the widespread environmental toxic metals. The δ-ALA-D inhibition mechanism by metals of Group 10 (Ni, Pd, and Pt) and 11 (Cu, Ag, and Au) of the periodic table has not yet been determined. The objective of this study was to characterize the molecular mechanism of δ-ALA-D inhibition caused by the elements of groups 10 and 11 using in vitro (δ-ALA-D activity from human erythrocytes) and in silico (docking simulations) methods. Our results showed that Ni(II) and Pd(II) caused a small inhibition (~ 10%) of the δ-ALA-D. Pt(II) and Pt(IV) significantly inhibited the enzyme (75% and 44%, respectively), but this inhibition was attenuated by Zn(II) and dithiothreitol (DTT). In group 11, all metals inhibited δ-ALA-D with great potency (~ 70-90%). In the presence of Zn(II) and DTT, the enzyme activity was restored to the control levels. The in silico molecular docking data suggest that the coordination of the ions Pt(II), Pt(IV), Cu(II), Ag(I), and Au(III) with thiolates groups from C135 and C143 residues from the δ-ALA-D active site are crucial to the enzyme inhibition. The results indicate that a possible mechanism of inhibition of δ-ALA-D by these metals may involve the replacement of the Zn(II) from the active site and/or the cysteinyl residue oxidation.

Discovering Highly Potent Molecules from an Initial Set of Inactives Using Iterative Screening

The versatility of similarity searching and quantitative structure-activity relationships to model the activity of compound sets within given bioactivity ranges (i.e., interpolation) is well established. However, their relative performance in the common scenario in early stage drug discovery where lots of inactive data but no active data points are available (i.e., extrapolation from the low-activity to the high-activity range) has not been thoroughly examined yet. To this aim, we have designed an iterative virtual screening strategy which was evaluated on 25 diverse bioactivity data sets from ChEMBL. We benchmark the efficiency of random forest (RF), multiple linear regression, ridge regression, similarity searching, and random selection of compounds to identify a highly active molecule in the test set among a large number of low-potency compounds. We use the number of iterations required to find this active molecule to evaluate the performance of each experimental setup. We show that linear and ridge regression often outperform RF and similarity searching, reducing the number of iterations to find an active compound by a factor of 2 or more. Even simple regression methods seem better able to extrapolate to high-bioactivity ranges than RF, which only provides output values in the range covered by the training set. In addition, examination of the scaffold diversity in the data sets used shows that in some cases similarity searching and RF require two times as many iterations as random selection depending on the chemical space covered in the initial training data. Lastly, we show using bioactivity data for COX-1 and COX-2 that our framework can be extended to multitarget drug discovery, where compounds are selected by concomitantly considering their activity against multiple targets. Overall, this study provides an approach for iterative screening where only inactive data are present in early stages of drug discovery in order to discover highly potent compounds and the best experimental set up in which to do so.

Virtual Chemical Libraries

Advances in computer processing speed and storage capacity have enabled researchers to generate virtual chemical libraries containing billions of molecules. While these numbers appear large, they are only a small fraction of the number of organic molecules that could potentially be synthesized. This review provides an overview of recent advances in the generation and use of virtual chemical libraries in medicinal chemistry. We also consider the practical implications of these libraries in drug discovery programs and highlight a number of current and future challenges.

Chemical space of Escherichia coli dihydrofolate reductase inhibitors: New approaches for discovering novel drugs for old bugs

Escherichia coli Dihydrofolate reductase is an important enzyme that is essential for the survival of the Gram-negative microorganism. Inhibitors designed against this enzyme have demonstrated application as antibiotics. However, either because of poor bioavailability of the small-molecules resulting from their inability to cross the double membrane in Gram-negative bacteria or because the microorganism develops resistance to the antibiotics by mutating the DHFR target, discovery of new antibiotics against the enzyme is mandatory to overcome drug-resistance. This review summarizes the field of DHFR inhibition with special focus on recent efforts to effectively interface computational and experimental efforts to discover novel classes of inhibitors that target allosteric and active-sites in drug-resistant variants of EcDHFR.

Identification of a Water-Coordinating HER2 Inhibitor by Virtual Screening Using Similarity-Based Scoring

Human epidermal growth factor receptor 2 (HER2) is a validated breast cancer drug target for small molecule inhibitors that target the ATP-binding pocket of the kinase domain. In this work, a large-scale virtual screen was performed to a novel homology model of HER2, in a hypothesized “fully active” state, that considered water-mediated interactions during the prioritization of compounds for experimental testing. This screen led to the identification of a new inhibitor with micro molar affinity and potency (K_d = 7.0 μM, IC₅₀ = 4.6 μM). Accompanying molecular dynamics simulations showed that inhibitor binding likely involves water coordination through an important water-mediated network previously identified in our laboratory. The predicted binding geometry also showed a remarkable overlap with the crystallographic poses for two previously reported inhibitors of the related Chk1 kinase. Concurrent with the HER2 studies, we developed formalized computational protocols that leverage solvated footprints (per-residue interaction maps that include bridging waters) to identify ligands that can “coordinate” or “displace” key binding site waters. Proof-of-concept screens targeting HIVPR and PARP1 demonstrate that molecules with high footprint overlap can be effectively identified in terms of their coordination or displacement patterns relative to a known reference. Overall, the procedures developed as a result of this study should be useful for researchers targeting HER2 and, more generally, for any protein in which the identification of compounds that exploit binding site waters is desirable.

G-score: A function to solve the puzzle of modeling the protonation states of β-secretase binding pocket

The population density concept has emerged as a proposal for the analysis of molecular dynamics results, the key characteristic of population density is the evaluation of the simultaneous occurrence of a set of relevant parameters for a system. However, despite its statistical strength, selection of the tolerance level for the comparison of different models may appear as arbitrary. This work introduces the G-score, a function which summarizes and categorizes the results of population density analysis. Additionally, it incorporates parameters based on rmsd and dihedral angles, besides the protein-protein and protein-ligand interatomic distances conventionally used, which complement each other to provide a better description of the behavior of the system. These newly-proposed tools were applied to determine the most probable protonation state of the aspartic dyad of BACE1, Asp93 and Asp289, in the presence of three types of transition state inhibitors namely: reduced amides, tertiary carbinamines and hydroxyethylamines. The results show a full agreement between G-score values and population density charts, with the advantage of allowing a quick and direct comparison among all the considered models. We anticipate that the simplicity of calculating the parameters employed in this study will permit the extensive use of population density and the G-score for other molecular systems.