Drug Design and Discovery: Principles and Applications

Identification of potential inhibitors targeting yellow fever virus helicase through ligand and structure-based computational studies

Yellow fever is a flavivirus having plus-sensed RNA which encodes a single polyprotein. Host proteases cut this polyprotein into seven nonstructural proteins including a vital NS3 protein. The present study aims to identify the most effective inhibitor against the helicase (NS3) using different advanced ligand and structure-based computational studies. A set of 300 ligands was selected against helicase by chemical structural similarity model, which are similar to S-adenosyl-l-cysteine using infiniSee. This tool screens billions of compounds through a similarity search from in-built chemical spaces (CHEMriya, Galaxi, KnowledgeSpace and REALSpace). The pharmacophore was designed from ligands in the library that showed same features. According to the sequence of ligands, six compounds (29, 87, 99, 116, 148, and 208) were taken for pharmacophore designing against helicase protein. Subsequently, compounds from the library which showed the best pharmacophore shared-features were docked using FlexX functionality of SeeSAR and their optibrium properties were analyzed. Afterward, their ADME was improved by replacing the unfavorable fragments, which resulted in the generation of new compounds. The selected best compounds (301, 302, 303 and 304) were docked using SeeSAR and their pharmacokinetics and toxicological properties were evaluated using SwissADME. The optimal inhibitor for yellow fever helicase was 2-amino-N-(4-(dimethylamino)thiazol-2-yl)-4-methyloxazole-5-carboxamide (302), which exhibits promising potential for drug development.

Investigation of the inhibitory peptide effect as novel strategy in cancer treatment: Targeting the tetramerization of lactate dehydrogenase A

The Warburg effect is detected in numerous types of solid tumors and is characterized by the enhancement of glucose consumption and an increase in the rate of glycolysis in cancer cells. Lactate dehydrogenase A (LDHA) plays a critical role in the Warburg effect and to date, a number of LDHA inhibitors have been identified that act as competitive inhibitors of both the substrate pyruvate and the coenzyme NADH. In our previous study, a set of novel peptides were designed using in silico structure-based method with the aim of inhibiting the activity of LDHA by disrupting the interaction between its enzyme subunits. In the present study employed in vitro and in vivo techniques, to explore the influence of these peptides on the induction of cytotoxicity effect on cancer cell lines. Our findings revealed that the designed peptide effectively inhibits LDHA, leading to the induction of apoptosis and G2/M cell cycle arrest. Finally, we also analyzed the impact of the designed peptide on Balb/c mice, confirming the results of in silico and in vitro studies. Ultimately, the designed peptide had the ability to induce apoptosis in cancer cells by LDHA inhibition.

3D bioprinting for the construction of drug testing models-development strategies and regulatory concerns

A drug to be successfully launched in the market requires a significant amount of capital, resources and time, where the unsuccessful results in the last stages lead to catastrophic failure for discovering drugs. This is the very reason which calls for the invention of innovative models that can closely mimic the human in vivo model for producing reliable results. Throughout the innovation line, there has been improvement in the rationale in silico designing but yet there is requirement for in vitro-in vivo correlations. During the evolving of the drug testing models, the 3D models produced by different methods have been proven to produce better results than the traditional 2D models. However, the in vitro fabrications of live tissues are still bottleneck in realizing their complete potential. There is an urgent need for the development of single, standard and simplified in vitro 3D tissue models that can be reliable for investigating the biological and pathological aspects of drug discovery, which is yet to be achieved. The existing pre-clinical models have considerable drawbacks despite being the gold standard in pre-clinical research. The major drawback being the interspecies differences and low reliability on the generated results. This gap could be overcome by the fabrication of bioengineered human disease models for drug screening. The advancement in the fabrication of 3D models will provide a valuable tool in screening drugs at different stages as they are one step closer to bio-mimic human tissues. In this review, we have discussed on the evolution of preclinical studies, and different models, including mini tissues, spheroids, organoids, bioengineered three dimensional models and organs on chips. Furthermore, we provide details of different disease models fabricated across various organs and their applications. In addition to this, the review also focuses on the limitations and the current prospects of the role of three dimensionally bioprinted models in drug screening and development.

Analyzing Oxygen Atom Distribution in FDA-Approved Drugs to Enhance Drug Discovery Strategies

Despite advancements in molecular design rules and understanding biochemical processes, the field of drug design and discovery seeks to minimize the number and duration of synthesis-testing cycles to convert lead compounds into drug candidates. A promising strategy involves gaining insightful understanding of key heteroatoms such as oxygen and nitrogen. This work presents a comprehensive analysis of oxygen atoms in approved drugs, aiming to streamline drug design and discovery efforts. The study examines the frequency, distribution, prevalence, and diversity of oxygen atoms in a dataset of 2049 small molecules approved by the FDA and other agencies. The analysis focuses on various types of oxygen atoms, including sp3, sp2-hybridized, ring, and nonring. In general, existence of sp3-O slightly outperforms sp2-O, which is associated with balancing various factors such as flexibility, solubility, stability, and pharmacokinetics, in addition to activity and selectivity. In approved drugs, majority of oxygen atoms are present within 4 Å from the COM of the molecule. This analysis offers valuable understanding of oxygen distribution, which could be used during the multiparameter optimization process, facilitating the transformation of a hit/lead compound into a potential drug candidate.

Mfgnn: Multi-Scale Feature-Attentive Graph Neural Networks for Molecular Property Prediction

In the realm of artificial intelligence-driven drug discovery (AIDD), accurately predicting the influence of molecular structures on their properties is a critical research focus. While deep learning models based on graph neural networks (GNNs) have made significant advancements in this area, prior studies have primarily concentrated on molecule-level representations, often neglecting the impact of functional group structures and the potential relationships between fragments on molecular property predictions. To address this gap, we introduce the multi-scale feature attention graph neural network (MfGNN), which enhances traditional atom-based molecular graph representations by incorporating fragment-level representations derived from chemically synthesizable BRICS fragments. MfGNN not only effectively captures both the structural information of molecules and the features of functional groups but also pays special attention to the potential relationships between fragments, exploring how they collectively influence molecular properties. This model integrates two core mechanisms: a graph attention mechanism that captures embeddings of molecules and functional groups, and a feature extraction module that systematically processes BRICS fragment-level features to uncover relationships among the fragments. Our comprehensive experiments demonstrate that MfGNN outperforms leading machine learning and deep learning models, achieving state-of-the-art performance in 8 out of 11 learning tasks across various domains, including physical chemistry, biophysics, physiology, and toxicology. Furthermore, ablation studies reveal that the integration of multi-scale feature information and the feature extraction module enhances the richness of molecular features, thereby improving the model's predictive capabilities.

First report on chemometrics-driven multilayered lead prioritization in addressing oxysterol-mediated overexpression of G protein-coupled receptor 183

Contemporary research has convincingly demonstrated that upregulation of G protein-coupled receptor 183 (GPR183), orchestrated by its endogenous agonist, 7α,25-dihydroxyxcholesterol (7α,25-OHC), leads to the development of cancer, diabetes, multiple sclerosis, infectious, and inflammatory diseases. A recent study unveiled the cryo-EM structure of 7α,25-OHC bound GPR183 complex, presenting an untapped opportunity for computational exploration of potential GPR183 inhibitors, which served as our inspiration for the current work. A predictive and validated two-dimensional QSAR model using genetic algorithm (GA) and multiple linear regression (MLR) on experimental GPR183 inhibition data was developed. QSAR study highlighted that structural features like dissimilar electronegative atoms, quaternary carbon atoms, and CH2RX fragment (X: heteroatoms) influence positively, while the existence of oxygen atoms with a topological separation of 3, negatively affects GPR183 inhibitory activity. Post assessment of true external set prediction capability, the MLR model was deployed to screen 12,449 DrugBank compounds, followed by a screening pipeline involving molecular docking, druglikeness, ADMET, protein-ligand stability assessment using deep learning algorithm, molecular dynamics, and molecular mechanics. The current findings strongly evidenced DB05790 as a potential lead for prospective interference of oxysterol-mediated GPR183 overexpression, warranting further in vitro and in vivo validation.

Comparative assessment of physics-based in silico methods to calculate relative solubilities

Relative solubilities, i.e. whether a given molecule is more soluble in one solvent compared to others, is a critical parameter for pharmaceutical and agricultural formulation development and chemical synthesis, material science, and environmental chemistry. In silico predictions of this crucial variable can help reducing experiments, waste of solvents and synthesis optimization. In this study, we evaluate the performance of different physics-based methods for predicting relative solubilities. Our assessment involves quantum mechanics-based COSMO-RS and molecular dynamics-based free energy methods using OPLS4, the open-source OpenFF Sage, and GAFF force fields, spanning over 200 solvent-solute combinations. Our investigation highlights the important role of compound multimerization, an effect which must be accounted for to obtain accurate relative solubility predictions. The performance landscape of these methods is varied, with significant differences in precision depending on both the method used and the solute considered, thereby offering an improved understanding of the predictive power of physics-based methods in chemical research.

Silver Nanoparticles (AgNPs) Uptake by Caveolae-Dependent Endocytosis is Responsible for Their Selective Effect Towards Castration Resistant Prostate Cancer

Purpose

Castration Resistant Prostate Cancer (CRPC) is characterized by poor prognosis and limited therapeutic options. AgNPs functionalized with glucose (G-AgNPs) were observed cytotoxic to CRPC cell lines (PC-3 and Du-145) and not LNCaP. This study aims to evaluate AgNPs and G-AgNPs' uptake mechanisms in these cells and understand their role in the selective effect against CRPC cells.

Methods

Uptake of AgNPs and G-AgNPs was assessed through transmission electron microscopy (TEM). A microRNA (miRNAs) analysis approach was used to uncover the main molecular differences responsible for the endocytic mechanisms' regulation. Caveolin (Cav) 1 and 2 mRNA and protein levels were assessed in the three cell lines. Caveolae-dependent endocytosis was inhibited with genistein or siCav1- and siCav2- in PC-3 and Du-145 and resazurin assay was used to evaluate viability after AgNPs and G-AgNPs administration. Caveolae-dependent endocytosis was induced with Cav1+ and Cav2+ plasmids in LNCaP, resazurin assay was used to evaluate viability after AgNPs and G-AgNPs administration and TEM to assess their location.

Results

AgNPs and G-AgNPs were not uptaked by LNCaP. miRNA analysis revealed 37 upregulated and 90 downregulated miRNAs. Functional enrichment analysis of miRNAs' targets resulted in enrichment of terms related to endocytosis and caveolae. We observed that Cav1 and Cav2 are not expressed in LNCaP. Inhibiting caveolae-dependent endocytosis in Du-145 and PC-3 led to a significative reduction of cytotoxic capacity of AgNPs and G-AgNPs and induction of caveolae-dependent endocytosis in LNCaP lead to a significative increase as well as their uptake by cells.

Conclusion

This study shows the potential of these AgNPs as a new therapeutic approach directed to CRPC patients, uncovers caveolae-dependent endocytosis as the uptake mechanism of these AgNPs and highlights deregulation of Cav1 and Cav2 expression as a key difference in hormone sensitive and resistant PCa cells which may be responsible for drug resistance.

Revolutionizing Drug Discovery: The Impact of Distinct Designs and Biosensor Integration in Microfluidics-Based Organ-on-a-Chip Technology

Traditional drug development is a long and expensive process with high rates of failure. This has prompted the pharmaceutical industry to seek more efficient drug development frameworks, driving the emergence of organ-on-a-chip (OOC) based on microfluidic technologies. Unlike traditional animal experiments, OOC systems provide a more accurate simulation of human organ microenvironments and physiological responses, therefore offering a cost-effective and efficient platform for biomedical research, particularly in the development of new medicines. Additionally, OOC systems enable quick and real-time analysis, high-throughput experimentation, and automation. These advantages have shown significant promise in enhancing the drug development process. The success of an OOC system hinges on the integration of specific designs, manufacturing techniques, and biosensors to meet the need for integrated multiparameter datasets. This review focuses on the manufacturing, design, sensing systems, and applications of OOC systems, highlighting their design and sensing capabilities, as well as the technical challenges they currently face.

A comprehensive review of artificial intelligence for pharmacology research

With the innovation and advancement of artificial intelligence, more and more artificial intelligence techniques are employed in drug research, biomedical frontier research, and clinical medicine practice, especially, in the field of pharmacology research. Thus, this review focuses on the applications of artificial intelligence in drug discovery, compound pharmacokinetic prediction, and clinical pharmacology. We briefly introduced the basic knowledge and development of artificial intelligence, presented a comprehensive review, and then summarized the latest studies and discussed the strengths and limitations of artificial intelligence models. Additionally, we highlighted several important studies and pointed out possible research directions.

Phytochemicals in Drug Discovery-A Confluence of Tradition and Innovation

Phytochemicals have a long and successful history in drug discovery. With recent advancements in analytical techniques and methodologies, discovering bioactive leads from natural compounds has become easier. Computational techniques like molecular docking, QSAR modelling and machine learning, and network pharmacology are among the most promising new tools that allow researchers to make predictions concerning natural products' potential targets, thereby guiding experimental validation efforts. Additionally, approaches like LC-MS or LC-NMR speed up compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, thus providing invaluable information to understand how phytochemicals interact with potential targets in the body. An emerging computational approach is machine learning involving QSAR modelling and deep neural networks that interrelate phytochemical properties with diverse physiological activities such as antimicrobial or anticancer effects.

Anti-aging and immunomodulatory role of caffeine in Drosophila larvae

Drug repurposing is a promising approach to identify new pharmacological indications for drugs that have already been established. However, there is still a limitation in the availability of a high-throughput in vivo preclinical system that is suitable for screening and investigating new pharmacological indications. The aim of this study was to introduce the application of Drosophila larvae as an in vivo platform to screen drug candidates with anti-aging and immunomodulatory activities. To determine whether Drosophila larvae can be utilized for assessing anti-aging and immunomodulatory activities, phenotypical and molecular assays were conducted using wildtype and mutant lines of Drosophila. The utilization of mutant lines (PGRP-LBΔ and Psh[1];;ModSP[KO]) mimics the autoinflammatory and immunodeficient conditions in humans, thereby enabling a thorough investigation of the effects of various compounds. The phenotypical assay was carried out using survival and locomotor observation in Drosophila larvae and adult flies. Meanwhile, the molecular assay was conducted using the RT-qPCR method. In vivo survival analysis revealed that caffeine was relatively safe for Drosophila larvae and exhibited the ability to extend Drosophila lifespan compared to the untreated controls, suggesting its anti-aging properties. Further analysis using the RT-qPCR method demonstrated that caffeine treatment induced transcriptional changes in the Drosophila larvae, particularly in the downstream of NF-κB and JAK-STAT pathways, two distinct immune-related pathways homologue to humans. In addition, caffeine enhanced the survival of Drosophila autoinflammatory model, further implying its immunosuppressive activity. Nevertheless, this compound had minimal to no effect on the survival of Staphylococcus aureus-infected wildtype and immunodeficient Drosophila, refuting its antibacterial and immunostimulant activities. Overall, our results suggest that the anti-aging and immunosuppressive activities of caffeine observed in Drosophila larvae align with those reported in mammalian model systems, emphasizing the suitability of Drosophila larvae as a model organism in drug repurposing endeavors, particularly for the screening of newly discovered chemical entities to assess their immunomodulatory activities before proceedings to investigations in mammalian animal models.

Artificial Intelligence in Drug Identification and Validation: A Scoping Review

The end-to-end process in the discovery of drugs involves therapeutic candidate identification, validation of identified targets, identification of hit compound series, lead identification and optimization, characterization, and formulation and development. The process is lengthy, expensive, tedious, and inefficient, with a large attrition rate for novel drug discovery. Today, the pharmaceutical industry is focused on improving the drug discovery process. Finding and selecting acceptable drug candidates effectively can significantly impact the price and profitability of new medications. Aside from the cost, there is a need to reduce the end-to-end process time, limiting the number of experiments at various stages. To achieve this, artificial intelligence (AI) has been utilized at various stages of drug discovery. The present study aims to identify the recent work that has developed AI-based models at various stages of drug discovery, identify the stages that need more concern, present the taxonomy of AI methods in drug discovery, and provide research opportunities. From January 2016 to September 1, 2023, the study identified all publications that were cited in the electronic databases including Scopus, NCBI PubMed, MEDLINE, Anthropology Plus, Embase, APA PsycInfo, SOCIndex, and CINAHL. Utilising a standardized form, data were extracted, and presented possible research prospects based on the analysis of the extracted data.

Network Pharmacology Approach to Identify the Calotropis Phytoconstituents' Potential Epileptic Targets and Evaluation of Molecular Docking, MD Simulation, and MM-PBSA Performance

Epilepsy originates from unusual electrical rhythm within brain cells, causes seizures. Calotropis species have been utilized to treat a wide spectrum of ailments since antiquity. Despite chemical and biological investigations, there have been minimal studies on their anticonvulsant activity, and the molecular targets of this plant constituents are unexplored. This study aimed to investigate the plausible epileptic targets of Calotropis phytoconstituents through network pharmacology, and to evaluate their binding strength and stability with the identified targets. In detail, 125 phytoconstituents of the Calotropis plant (C. procera and C. gigantea) were assessed for their drug-likeness (DL), blood-brain-barrier (BBB) permeability and oral bioavailability (OB). Network analysis revealed that targets PTGS2 and PPAR-γ were ranked first and fourth, respectively, among the top ten hub genes significantly linked with antiepileptic drug targets. Additionally, docking, molecular dynamic (MD) simulation, and Molecular Mechanics-Poisson-Boltzmann Surface Area (MM-PBSA) were employed to validate the compound-gene interactions. Docking studies suggested ergost-5-en-3-ol, stigmasterol and β-sitosterol exhibit stronger binding affinity and favorable interactions than co-crystallized ligands with both the targets. Furthermore, both MD simulations and MM-PBSA calculations substantiated the docking results. Combined data revealed that Calotropis phytoconstituents ergost-5-en-3-ol, stigmasterol, and β-sitosterol might be the best inhibitors of both PTGS2 and PPAR-γ.

Pre-training molecular representation model with spatial geometry for property prediction

AI-enhanced bioinformatics and cheminformatics pivots on generating increasingly descriptive and generalized molecular representation. Accurate prediction of molecular properties needs a comprehensive description of molecular geometry. We design a novel Graph Isomorphic Network (GIN) based model integrating a three-level network structure with a dual-level pre-training approach that aligns the characteristics of molecules. In our Spatial Molecular Pre-training (SMPT) Model, the network can learn implicit geometric information in layers from lower to higher according to the dimension. Extensive evaluations against established baseline models validate the enhanced efficacy of SMPT, with notable accomplishments in classification tasks. These results emphasize the importance of spatial geometric information in molecular representation modeling and demonstrate the potential of SMPT as a valuable tool for property prediction.

Synthesis, Molecular Docking, Molecular Dynamic Simulation Studies, and Antitubercular Activity Evaluation of Substituted Benzimidazole Derivatives

Tuberculosis, also known as TB, is a widespread bacterial infection that remains a significant global health issue. This study focuses on conducting a thorough investigation into the synthesis, evaluation of anti-Tb activity, molecular docking, and molecular dynamic simulation of substituted benzimidazole derivatives. A series of twelve substituted benzimidazole derivatives (1-12) were successfully synthesized, employing a scaffold consisting of electron-withdrawing and electron-donating groups. The newly synthesized compounds were defined by their FTIR, 1H NMR, and mass spectra. The microplate Alamar blue assay (MABA) was used to evaluate the antimycobacterial activity of the synthesized compound against Mycobacterium tuberculosis (Mtb). Compounds 7 (MIC = 0.8 g/mL) and 8 (MIC = 0.8 g/mL) demonstrated exceptional potential to inhibit M. tuberculosis compared to the standard drug (isoniazid). In addition, the synthesized compounds were docked with the Mtb KasA protein (PDB ID: 6P9K), and the results of molecular docking and molecular dynamic simulation confirmed the experimental results, as compounds 7 and 8 exhibited the highest binding energy of -7.36 and -7.17 kcal/mol, respectively. The simulation results such as the RMSD value, RMSF value, radius of gyration, and hydrogen bond analysis illustrated the optimum potential of compounds 7 and 8 to inhibit the M. tuberculosis strain. Hydrogen bond analysis suggested that compound 7 has greater stability and affinity towards the KasA protein compared to compound 8. Moreover, both compounds (7 and 8) were safe for acute inhalation and cutaneous sensitization. These two compounds have the potential to be potent M. tuberculosis inhibitors.

Overcoming Chemotherapy Resistance in Metastatic Cancer: A Comprehensive Review

The management of metastatic cancer is complicated by chemotherapy resistance. This manuscript provides a comprehensive academic review of strategies to overcome chemotherapy resistance in metastatic cancer. The manuscript presents background information on chemotherapy resistance in metastatic cancer cells, highlighting its clinical significance and the current challenges associated with using chemotherapy to treat metastatic cancer. The manuscript delves into the molecular mechanisms underlying chemotherapy resistance in subsequent sections. It discusses the genetic alterations, mutations, and epigenetic modifications that contribute to the development of resistance. Additionally, the role of altered drug metabolism and efflux mechanisms, as well as the activation of survival pathways and evasion of cell death, are explored in detail. The strategies to overcome chemotherapy resistance are thoroughly examined, covering various approaches that have shown promise. These include combination therapy approaches, targeted therapies, immunotherapeutic strategies, and the repurposing of existing drugs. Each strategy is discussed in terms of its rationale and potential effectiveness. Strategies for early detection and monitoring of chemotherapy drug resistance, rational drug design vis-a-vis personalized medicine approaches, the role of predictive biomarkers in guiding treatment decisions, and the importance of lifestyle modifications and supportive therapies in improving treatment outcomes are discussed. Lastly, the manuscript outlines the clinical implications of the discussed strategies. It provides insights into ongoing clinical trials and emerging therapies that address chemotherapy resistance in metastatic cancer cells. The manuscript also explores the challenges and opportunities in translating laboratory findings into clinical practice and identifies potential future directions and novel therapeutic avenues. This comprehensive review provides a detailed analysis of strategies to overcome chemotherapy resistance in metastatic cancer. It emphasizes the importance of understanding the molecular mechanisms underlying resistance and presents a range of approaches for addressing this critical issue in treating metastatic cancer.

In silico Approaches for Exploring the Pharmacological Activities of Benzimidazole Derivatives: A Comprehensive Review

BACKGROUND

This article reviews computational research on benzimidazole derivatives. Cytotoxicity for all compounds against cancer cell lines was measured and the results revealed that many compounds exhibited high inhibitions. This research examines the varied pharmacological properties like anticancer, antibacterial, antioxidant, anti-inflammatory and anticonvulsant activities of benzimidazole derivatives. The suggested method summarises In silico research for each activity. This review examines benzimidazole derivative structure-activity relationships and pharmacological effects. In silico investigations can anticipate structural alterations and their effects on these derivative's pharmacological characteristics and efficacy through many computational methods. Molecular docking, molecular dynamics simulations and virtual screening help anticipate pharmacological effects and optimize chemical design. These trials will improve lead optimization, target selection, and ADMET property prediction in drug development. In silico benzimidazole derivative studies will be assessed for gaps and future research. Prospective studies might include empirical verification, pharmacodynamic analysis, and computational methodology improvement.

OBJECTIVES

This review discusses benzimidazole derivative In silico research to understand their specific pharmacological effects. This will help scientists design new drugs and guide future research.

METHODS

Latest, authentic and published reports on various benzimidazole derivatives and their activities are being thoroughly studied and analyzed.

RESULT

The overview of benzimidazole derivatives is more comprehensive, highlighting their structural diversity, synthetic strategies, mechanisms of action, and the computational tools used to study them.

CONCLUSION

In silico studies help to understand the structure-activity relationship (SAR) of benzimidazole derivatives. Through meticulous alterations of substituents, ring modifications, and linker groups, this study identified the structural factors influencing the pharmacological activity of benzimidazole derivatives. These findings enable the rational design and optimization of more potent and selective compounds.

Leveraging nitrogen occurrence in approved drugs to identify structural patterns

BACKGROUND

The process of drug development and discovery is costly and slow. Although an understanding of molecular design principles and biochemical processes has progressed, it is essential to minimize synthesis-testing cycles. An effective approach is to analyze key heteroatoms, including oxygen and nitrogen. Herein, we present an analysis focusing on the utilization of nitrogen atoms in approved drugs.

RESEARCH DESIGN AND METHODS

The present work examines the frequency, distribution, prevalence, and diversity of nitrogen atoms in a dataset comprising 2,049 small molecules approved by different regulatory agencies (FDA and others). Various types of nitrogen atoms, such as sp3-, sp2-, sp-hybridized, planar, ring, and non-ring are included in this investigation.

RESULTS

The results unveil both previously reported and newly discovered patterns of nitrogen atom distribution around the center of mass in the majority of drug molecules.

CONCLUSIONS

This study has highlighted intriguing trends in the role of nitrogen atoms in drug design and development. The majority of drugs contain 1-3 nitrogen atoms within 5Å from the center of mass (COM) of a molecule, with a higher preference for the ring and planar nitrogen atoms. The results offer invaluable guidance for the multiparameter optimization process, thus significantly contributing toward the conversion of lead compounds into potential drug candidates.

Discovery of druggable potent inhibitors of serine proteases and farnesoid X receptor by ligand-based virtual screening to obstruct SARS-CoV-2

The coronavirus, a subfamily of the coronavirinae family, is an RNA virus with over 40 variations that can infect humans, non-human mammals and birds. There are seven types of human coronaviruses, including SARS-CoV-2, is responsible for the recent COVID-19 pandemic. The current study is focused on the identification of drug molecules for the treatment of COVID-19 by targeting human proteases like transmembrane serine protease 2 (TMPRSS2), furin, cathepsin B, and a nuclear receptor named farnesoid X receptor (FXR). TMPRSS2 and furin help in cleaving the spike protein of the SARS-CoV-2 virus, while cathepsin B plays a critical role in the entry and pathogenesis. FXR, on the other hand, regulates the expression of ACE2, and its inhibition can reduce SARS-CoV-2 infection. By inhibiting these four protein targets with non-toxic inhibitors, the entry of the infectious agent into host cells and its pathogenesis can be obstructed. We have used the BioSolveIT suite for pharmacophore-based computational drug designing. A total of 1611 ligands from the ligand library were docked with the target proteins to obtain potent inhibitors on the basis of pharmacophore. Following the ADMET analysis and protein ligand interactions, potent and druggable inhibitors of the target proteins were obtained. Additionally, toxic substructures and the less toxic route of administration of the most potent inhibitors in rodents were also determined computationally. Compounds namely N-(diaminomethylene)-2-((3-((1R,3R)-3-(2-(methoxy(methyl)amino)-2-oxoethyl)cyclopentyl)propyl)amino)-2-oxoethan-1-aminium (26), (1R,3R)-3-(((2-ammonioethyl)ammonio)methyl)-1-((4-propyl-1H-imidazol-2-yl)methyl)piperidin-1-ium (29) and (1R,3R)-3-(((2-ammonioethyl)ammonio)methyl)-1-((1-propyl-1H-pyrazol-4-yl)methyl)piperidin-1-ium (30) were found as the potent inhibitors of TMPRSS2, whereas, 1-(1-(1-(1H-tetrazol-1-yl)cyclopropane-1‑carbonyl)piperidin-4-yl)azepan-2-one (6), (2R)-4-methyl-1-oxo-1-((7R,11S)-4-oxo-6,7,8,9,10,11-hexahydro-4H-7,11-methanopyrido[1,2-a]azocin-9-yl)pentan-2-aminium (12), 4-((1-(3-(3,5-dimethylisoxazol-4-yl)propanoyl)piperidin-4-yl)methyl)morpholin-4-ium (13), 1-(4,6-dimethylpyrimidin-2-yl)-N-(3-oxocyclohex-1-en-1-yl)piperidine-4-carboxamide (14), 1-(4-(1,5-dimethyl-1H-1,2,4-triazol-3-yl)piperidin-1-yl)-3-(3,5-dimethylisoxazol-4-yl)propan-1-one (25) and N,N-dimethyl-4-oxo-4-((1S,5R)-8-oxo-5,6-dihydro-1H-1,5-methanopyrido[1,2-a][1,5]diazocin-3(2H,4H,8H)-yl)butanamide (31) inhibited the FXR preferentially. In case of cathepsin B, N-((5-benzoylthiophen-2-yl)methyl)-2-hydrazineyl-2-oxoacetamide (2) and N-([2,2'-bifuran]-5-ylmethyl)-2-hydrazineyl-2-oxoacetamide (7) were identified as the most druggable inhibitors whereas 1-amino-2,7-diethyl-3,8-dioxo-6-(p-tolyl)-2,3,7,8-tetrahydro-2,7-naphthyridine-4‑carbonitrile (5) and (R)-6-amino-2-(2,3-dihydroxypropyl)-1H-benzo[de]isoquinoline-1,3(2H)-dione (20) were active against furin.

Predicting absolute aqueous solubility by applying a machine learning model for an artificially liquid-state as proxy for the solid-state

In this study, we use machine learning algorithms with QM-derived COSMO-RS descriptors, along with Morgan fingerprints, to predict the absolute solubility of drug-like compounds. The QM-derived descriptors account for the molecular properties of the solute, i.e., the solute-solute interactions in an artificial-liquid-state (super-cooled liquid), and the solute-solvent interactions in solution. We employ two main approaches to predict solubility: (i) a hypothetical pathway that involves melting the solute at room temperature T = T¯ ([Formula: see text]) and mixing the artificially liquid solute into the solvent ([Formula: see text]). In this approach [Formula: see text] is predicted using machine learning models, and the [Formula: see text] is obtained from COSMO-RS calculations; (ii) direct solubility prediction using machine learning algorithms. The models were trained on a large number of Bayer in-house compounds for which water solubility data is available at physiological pH of 6.5 and ambient temperature. We also evaluated our models using external datasets from a solubility challenge. Our models present great improvements compared to the absolute solubility prediction with the QSAR model for the artificial liquid state as implemented in the COSMOtherm software, for both in-house and external datasets. We are furthermore able to demonstrate the superiority of QM-derived descriptors compared to cheminformatics descriptors. We finally present low-cost alternative models using fragment-based COSMOquick calculations with only marginal reduction in the quality of predicted solubility.

Decalepis hamiltonii root fraction alleviates CCl4 hepatotoxicity in a rat model

BACKGROUND

Decalepis hamiltonii (D. hamiltonii) is Indian folk medicine in herbal preparations, to reduce appetite, and cures dysentery, bronchitis, uterine hemorrhage, and other ailments.

OBJECTIVE

The current investigation focused on the hepatoprotective effect of D. hamiltonii roots fractions against liver damage.

MATERIALS AND METHODS

The current research discussed the fraction from D. hamiltonii root extracts was used. Male Wistar rats (albino strain) were grouped into 4 distinct groups of six animals each. Group I: plain water and vehicle whereas Group II (CCl4 control): CCl4 (1 ml/kg, 20 % v/v in olive oil) over 7 days and vehicle; Over 7 days, Group III received Silymarin 100 mg/kg/day and tap water with 20 % v/v of CCl4, whereas Group IV (treatment group) received DHE 50 mg/kg/day, 100 mg/kg/day, and water. Assessment of biochemical parameters, Mitochondrial modulation, gene expression analysis, and RT-PCR, was used to estimate the protective action of DHEF in CCl4-intoxicated rats.

RESULTS

The administration of CCl4 increased levels of total bilirubin (0.63 ± 0.97 mg/dl) plasma amino transferases (110.36 ± 1.13 U/L, 86.56 ± 2.41 U/L and 1.51 ± 1.36 mg/dl respectively) which were mitigated by D. hamiltonii treatment. Activity like Lipid peroxidation and content of nitric oxide also augmented, while the antioxidant action measured by GSH (9.64 ± 0.18 U/mg protein), SOD (3.69 ± 0.22 U/mg protein), and CAT (1.47 ± 0.01 U/mg protein) was reduced. Decalepis hamiltonii root provided substantial restoration of GSH (14.92 ± 0.04 nmol/gm protein), SOD (4.20 ± 0.18 U/mg protein), and CAT (2.71 ± 0.04 U/mg protein) levels. In addition, the acute phase reactants stimulated by CCl4 administration enhanced mRNA expressions of IL-6, IL-10, TNF-a, NF-κβ, and COX-2, which were enhanced by D. hamiltonii treatment.

CONCLUSIONS

In summary, DHEF protects the liver against CCl4-induced damage, possibly by mitochondrial modulation mechanism. These findings indicate that D. hamiltonii significantly moderates oxidative stress of CCl4-induced hepatotoxicity.

Recent advances on molecular dynamics-based techniques to address drug membrane permeability with atomistic detail

Several factors affect the passive membrane permeation of small molecules, including size, charge, pH, or the presence of specific chemical groups. Understanding these features is paramount to identifying or designing drug candidates with optimal ADMET properties and this can be achieved through experimental/knowledge-based methodologies or using computational approaches. Empirical methods often lack detailed information about the underlying molecular mechanism. In contrast, Molecular Dynamics-based approaches are a powerful strategy, providing an atomistic description of this process. This technique is continuously growing, featuring new related methodologies. In this work, the recent advances in this research area will be discussed.

Identification of Potent Inhibitors Targeting EGFR and HER3 for Effective Treatment of Chemoresistance in Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) is the most common form of lung cancer. Despite the existence of various therapeutic options, NSCLC is still a major health concern due to its aggressive nature and high mutation rate. Consequently, HER3 has been selected as a target protein along with EGFR because of its limited tyrosine kinase activity and ability to activate PI3/AKT pathway responsible for therapy failure. We herein used a BioSolveIT suite to identify potent inhibitors of EGFR and HER3. The schematic process involves screening of databases for constructing compound library comprising of 903 synthetic compounds (602 for EGFR and 301 for HER3) followed by pharmacophore modeling. The best docked poses of compounds with the druggable binding site of respective proteins were selected according to pharmacophore designed by SeeSAR version 12.1.0. Subsequently, preclinical analysis was performed via an online server SwissADME and potent inhibitors were selected. Compound 4k and 4m were the most potent inhibitors of EGFR while 7x effectively inhibited the binding site of HER3. The binding energies of 4k, 4m, and 7x were -7.7, -6.3 and -5.7 kcal/mol, respectively. Collectively, 4k, 4m and 7x showed favorable interactions with the most druggable binding sites of their respective proteins. Finally, in silico pre-clinical testing by SwissADME validated the non-toxic nature of compounds 4k, 4m and 7x providing a promising treatment option for chemoresistant NSCLC.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Dockey: a modern integrated tool for large-scale molecular docking and virtual screening

Molecular docking is a structure-based and computer-aided drug design approach that plays a pivotal role in drug discovery and pharmaceutical research. AutoDock is the most widely used molecular docking tool for study of protein-ligand interactions and virtual screening. Although many tools have been developed to streamline and automate the AutoDock docking pipeline, some of them still use outdated graphical user interfaces and have not been updated for a long time. Meanwhile, some of them lack cross-platform compatibility and evaluation metrics for screening lead compound candidates. To overcome these limitations, we have developed Dockey, a flexible and intuitive graphical interface tool with seamless integration of several useful tools, which implements a complete docking pipeline covering molecular sanitization, molecular preparation, paralleled docking execution, interaction detection and conformation visualization. Specifically, Dockey can detect the non-covalent interactions between small molecules and proteins and perform cross-docking between multiple receptors and ligands. It has the capacity to automatically dock thousands of ligands to multiple receptors and analyze the corresponding docking results in parallel. All the generated data will be kept in a project file that can be shared between any systems and computers with the pre-installation of Dockey. We anticipate that these unique characteristics will make it attractive for researchers to conduct large-scale molecular docking without complicated operations, particularly for beginners. Dockey is implemented in Python and freely available at https://github.com/lmdu/dockey.

An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects

Approved drugs for sale must be effective and safe, implying that the drug's advantages outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common reasons for drug failure that may halt the whole drug discovery pipeline. The side effects might vary from minor concerns like a runny nose to potentially life-threatening issues like liver damage, heart attack, and death. Therefore, predicting the side effects of the drug is vital in drug development, discovery, and design. Supervised machine learning-based side effects prediction task has recently received much attention since it reduces time, chemical waste, design complexity, risk of failure, and cost. The advancement of supervised learning approaches for predicting side effects have emerged as essential computational tools. Supervised machine learning technique provides early information on drug side effects to develop an effective drug based on drug properties. Still, there are several challenges to predicting drug side effects. Thus, a near-exhaustive survey is carried out in this paper on the use of supervised machine learning approaches employed in drug side effects prediction tasks in the past two decades. In addition, this paper also summarized the drug descriptor required for the side effects prediction task, commonly utilized drug properties sources, computational models, and their performances. Finally, the research gap, open problems, and challenges for the further supervised learning-based side effects prediction task have been discussed.

CADD, AI and ML in drug discovery: A comprehensive review

Computer-aided drug design (CADD) is an emerging field that has drawn a lot of interest because of its potential to expedite and lower the cost of the drug development process. Drug discovery research is expensive and time-consuming, and it frequently took 10-15 years for a drug to be commercially available. CADD has significantly impacted this area of research. Further, the combination of CADD with Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) technologies to handle enormous amounts of biological data has reduced the time and cost associated with the drug development process. This review will discuss how CADD, AI, ML, and DL approaches help identify drug candidates and various other steps of the drug discovery process. It will also provide a detailed overview of the different in silico tools used and how these approaches interact.

Modern drug discovery for inflammatory bowel disease: The role of computational methods

Inflammatory bowel diseases (IBDs) comprising ulcerative colitis, Crohn’s disease and microscopic colitis are characterized by chronic inflammation of the gastrointestinal tract. IBD has spread around the world and is becoming more prevalent at an alarming rate in developing countries whose societies have become more westernized. Cell therapy, intestinal microecology, apheresis therapy, exosome therapy and small molecules are emerging therapeutic options for IBD. Currently, it is thought that low-molecular-mass substances with good oral bio-availability and the ability to permeate the cell membrane to regulate the action of elements of the inflammatory signaling pathway are effective therapeutic options for the treatment of IBD. Several small molecule inhibitors are being developed as a promising alternative for IBD therapy. The use of highly efficient and time-saving techniques, such as computational methods, is still a viable option for the development of these small molecule drugs. The computer-aided (in silico) discovery approach is one drug development technique that has mostly proven efficacy. Computational approaches when combined with traditional drug development methodology dramatically boost the likelihood of drug discovery in a sustainable and cost-effective manner. This review focuses on the modern drug discovery approaches for the design of novel IBD drugs with an emphasis on the role of computational methods. Some computational approaches to IBD genomic studies, target identification, and virtual screening for the discovery of new drugs and in the repurposing of existing drugs are discussed.

Anticancer Applications of Essential Oils Formulated into Lipid-Based Delivery Nanosystems

The use of natural compounds is becoming increasingly popular among patients, and there is a renewed interest among scientists in nature-based bioactive agents. Traditionally, herbal drugs can be taken directly in the form of teas/decoctions/infusions or as standardized extracts. However, the disadvantages of natural compounds, especially essential oils, are their instability, limited bioavailability, volatility, and often irritant/allergenic potential. However, these active substances can be stabilized by encapsulation and administered in the form of nanoparticles. This brief overview summarizes the latest results of the application of nanoemulsions, liposomes, solid lipid nanoparticles, and nanostructured lipid carriers used as drug delivery systems of herbal essential oils or used directly for their individual secondary metabolites applicable in cancer therapy. Although the discussed bioactive agents are not typical compounds used as anticancer agents, after inclusion into the aforesaid formulations improving their stability and bioavailability and/or therapeutic profile, they indicated anti-tumor activity and became interesting agents with cancer treatment potential. In addition, co-encapsulation of essential oils with synthetic anticancer drugs into nanoformulations with the aim to achieve synergistic effect in chemotherapy is discussed.

The Current Perspectives in Clinical Research: Computer-Assisted Drug Designing, Ethics, and Good Clinical Practice

In the era of emerging microbial and non-communicable diseases and re-emerging microbial infections, the medical fraternity and the public are plagued by under-preparedness. It is evident by the severity of the Coronavirus disease (COVID-19) pandemic that novel microbial diseases are a challenge and are challenging to control. This is mainly attributed to the lack of complete knowledge of the novel microbe’s biology and pathogenesis and the unavailability of therapeutic drugs and vaccines to treat and control the disease. Clinical research is the only answer utilizing which can handle most of these circumstances. In this review, we highlight the importance of computer-assisted drug designing (CADD) and the aspects of molecular docking, molecular superimposition, 3D-pharmacophore technology, ethics, and good clinical practice (GCP) for the development of therapeutic drugs, devices, and vaccines.

Toward the Identification of Natural Antiviral Drug Candidates against Merkel Cell Polyomavirus: Computational Drug Design Approaches

Merkel cell carcinoma (MCC) is a rare form of aggressive skin cancer mainly caused by Merkel cell polyomavirus (MCPyV). Most MCC tumors express MCPyV large T (LT) antigens and play an important role in the growth-promoting activities of oncoproteins. Truncated LT promotes tumorigenicity as well as host cell proliferation by activating the viral replication machinery, and inhibition of this protein in humans drastically lowers cellular growth linked to the corresponding cancer. Our study was designed with the aim of identifying small molecular-like natural antiviral candidates that are able to inhibit the proliferation of malignant tumors, especially those that are aggressive, by blocking the activity of viral LT protein. To identify potential compounds against the target protein, a computational drug design including molecular docking, ADME (absorption, distribution, metabolism, and excretion), toxicity, molecular dynamics (MD) simulation, and molecular mechanics generalized Born surface area (MM-GBSA) approaches were applied in this study. Initially, a total of 2190 phytochemicals isolated from 104 medicinal plants were screened using the molecular docking simulation method, resulting in the identification of the top five compounds having the highest binding energy, ranging between −6.5 and −7.6 kcal/mol. The effectiveness and safety of the selected compounds were evaluated based on ADME and toxicity features. A 250 ns MD simulation confirmed the stability of the selected compounds bind to the active site (AS) of the target protein. Additionally, MM-GBSA analysis was used to determine the high values of binding free energy (ΔG bind) of the compounds binding to the target protein. The five compounds identified by computational approaches, Paulownin (CID: 3084131), Actaealactone (CID: 11537736), Epigallocatechin 3-O-cinnamate (CID: 21629801), Cirsilineol (CID: 162464), and Lycoricidine (CID: 73065), can be used in therapy as lead compounds to combat MCPyV-related cancer. However, further wet laboratory investigations are required to evaluate the activity of the drugs against the virus.

Effects of Artemisia macrocephala Jacquem on Memory Deficits and Brain Oxidative Stress in Streptozotocin-Induced Diabetic Mice

Different species of Artemisia have been reported to have therapeutic potential in treating various health disorders, including diabetes and memory dysfunction. The present study was planned to evaluate the effects of Artemisia macrocephala Jacquem crude extract and its subfractions as antiamnesic agents in streptozotocin-induced (STZ) diabetic mice. The in vivo behavioral studies were performed using the Y Maze test and novel object recognition test (NORT) test at doses of 100 and 200 mg/kg of crude extract and 75 and 150 mg/kg of fractions. The in vitro and ex vivo anticholinesterase activities, along with biochemical parameters (superoxide dismutase, catalase, glutathione and lipid peroxidation) in the brain, were evaluated. Blood glucose levels were monitored with a glucometer; crude extract and fractions reduced the glucose level considerably, with some differences in the extent of their efficacies. The crude extract and fractions demonstrated significant inhibitory activity against cholinesterases (AChE and BuChE) in vitro. Crude, chloroform and ethyl acetate extract were found to be more potent than the other fractions, with IC50 of Crd-Am = 116.36 ± 1.48 and 240.52 ± 1.35 µg/mL, Chl-Am = 52.68 ± 1.09 and 57.45 ± 1.39 µg/mL and Et-Am = 75.19 ± 1.02 and 116.58 ± 1.09 µg/mL, respectively. Oxidative stress biomarkers like superoxide dismutase, catalase and glutathione levels were elevated, whereas MDA levels were reduced by crude extract and all fractions with little difference in their respective values. The Y-maze test and novel object recognition test demonstrated declines in memory impairment in groups (n = 6) treated with crude extract and fractions as compared to STZ diabetic (amnesic) group. The most active fraction, Chl-Am, was also subjected to isolation of bioactive compounds; three compounds were obtained in pure state and designated as AB-I, AB-II and AB-III. Overall, the results of the study showed that Artemisia macrocephala Jacquem enhanced the memory impairment associated with diabetes, elevated acetylcholine levels and ameliorated oxidative stress. Further studies are needed to explore the beneficial role of the secondary metabolites isolated in the present study as memory enhancers. Toxicological aspects of the extracts are also important and need to be evaluated in other animal models.

Commercial SARS-CoV-2 Targeted, Protease Inhibitor Focused and Protein-Protein Interaction Inhibitor Focused Molecular Libraries for Virtual Screening and Drug Design

Since December 2019, the new SARS-CoV-2-related COVID-19 disease has caused a global pandemic and shut down the public life worldwide. Several proteins have emerged as potential therapeutic targets for drug development, and we sought out to review the commercially available and marketed SARS-CoV-2-targeted libraries ready for high-throughput virtual screening (HTVS). We evaluated the SARS-CoV-2-targeted, protease-inhibitor-focused and protein-protein-interaction-inhibitor-focused libraries to gain a better understanding of how these libraries were designed. The most common were ligand- and structure-based approaches, along with various filtering steps, using molecular descriptors. Often, these methods were combined to obtain the final library. We recognized the abundance of targeted libraries offered and complimented by the inclusion of analytical data; however, serious concerns had to be raised. Namely, vendors lack the information on the library design and the references to the primary literature. Few references to active compounds were also provided when using the ligand-based design and usually only protein classes or a general panel of targets were listed, along with a general reference to the methods, such as molecular docking for the structure-based design. No receptor data, docking protocols or even references to the applied molecular docking software (or other HTVS software), and no pharmacophore or filter design details were given. No detailed functional group or chemical space analyses were reported, and no specific orientation of the libraries toward the design of covalent or noncovalent inhibitors could be observed. All libraries contained pan-assay interference compounds (PAINS), rapid elimination of swill compounds (REOS) and aggregators, as well as focused on the drug-like model, with the majority of compounds possessing their molecular mass around 500 g/mol. These facts do not bode well for the use of the reviewed libraries in drug design and lend themselves to commercial drug companies to focus on and improve.

Anti-Proliferative, Anti-Angiogenic and Safety Profiles of Novel HDAC Inhibitors for the Treatment of Metastatic Castration-Resistant Prostate Cancer

Metastatic castration-resistant prostate cancer (CRPC) has a five-year survival rate of 28%. As histone deacetylases (HDACs) are overexpressed in CRPC, the HDAC inhibitor suberoylanilide hydroxamic acid (SAHA) was trialled in CRPC patients but found to be toxic and inefficacious. Previously, we showed that novel HDAC inhibitors (Jazz90 (N1-hydroxy-N⁸-(4-(pyridine-2-carbothioamido)phenyl)octanediamide) and Jazz167 ([chlorido(η⁵-pentamethylcyclopentadieny[1–4](N1-hydroxy-N⁸-(4-(pyridine-2-carbothioamido-κ²N,S)phenyl)octanediamide)rhodium(III)] chloride) had a higher cancer-to-normal-cell selectivity and superior anti-angiogenic effects in CRPC (PC3) cells than SAHA. Thus, this study aimed to further investigate the efficacy and toxicity of these compounds. HUVEC tube formation assays revealed that Jazz90 and Jazz167 significantly reduced meshes and segment lengths in the range of 55–88 and 43–64%, respectively. However, Jazz90 and Jazz167 did not affect the expression of epithelial-to-mesenchymal transitioning markers E-cadherin and vimentin. Jazz90 and Jazz167 significantly inhibited the growth of PC3 and DU145 spheroids and reduced PC3 spheroid branching. Jazz90 and Jazz167 (25, 50 and 75 mg/kg/day orally for 21 days) were non-toxic in male BALB/c mice. The efficacy and safety of these compounds demonstrate their potential for further in vivo studies in CRPC models.

Expedient synthesis and anticancer evaluation of dual-action 9-anilinoacridine methyl triazene chimeras

The efficient synthesis of molecular hybrids including a DNA-intercalating 9-anilinoacridine (9-AnA) core and a methyl triazene DNA-methylating moiety is described. Nucleophilic aromatic substitution (S_N Ar) and electrophilic aromatic substitution (EAS) reactions using readily accessible starting materials provide a quick entry to novel bifunctional anticancer molecules. The chimeras were evaluated for their anticancer activity. Chimera 7b presented the highest antitumor activity at low micromolar IC₅₀ values in antiproliferative assays performed with various cancer cell lines. In comparison, compound 7b outperformed DNA-intercalating drugs like amsacrine and AHMA. Mechanistic studies of chimera 7b suggest a dual mechanism of action: methylation of the DNA-repairing protein MGMT associated with the triazene structural portion and Topo II inhibition by intercalation of the acridine core.

Some illuminating remarks on molecular genetics and genomics as well as drug development

Facing the explosive growth of biological sequences unearthed in the post-genomic age, one of the most important but also most difficult problems in computational biology is how to express a biological sequence with a discrete model or a vector, but still keep it with considerable sequence-order information or its special pattern. To deal with such a challenging problem, the ideas of "pseudo amino acid components" and "pseudo K-tuple nucleotide composition" have been proposed. The ideas and their approaches have further stimulated the birth for "distorted key theory", "wenxing diagram", and substantially strengthening the power in treating the multi-label systems, as well as the establishment of the famous "5-steps rule". All these logic developments are quite natural that are very useful not only for theoretical scientists but also for experimental scientists in conducting genetics/genomics analysis and drug development. Presented in this review paper are also their future perspectives; i.e., their impacts will become even more significant and propounding.