Building on a solid foundation: SAR and QSAR as a fundamental strategy to reduce animal testing

Quantitative structure-activity relationship predicting toxicity of pesticides towards Daphnia magna

Global pesticide usage reaching 2.7 million metric tons annually, brings a grave threat to non-target organisms, especially aquatic organisms, resulting in serious concerns. Predicting aquatic toxicity of pesticides towards Daphnia magna is significant. In this work, random forest (RF) algorithm, together with ten Dragon molecular descriptors, was successfully utilized to develop a quantitative structure-activity/toxicity relationship (QSAR/QSTR) model for the toxicity pEC50 of 745 pesticides towards Daphnia magna. The optimal QSTR model (RF Model I) based on the RF parameters of ntree = 50, mtry = 3 and nodesize = 5, yielded R2 = 0.877, MAE = 0.570, rms = 0.739 (training set of 596 pEC50), R2 = 0.807, MAE = 0.732, rms = 0.902 (test set of 149 pEC50), and R2 = 0.863, MAE = 0.602, rms = 0.774 (total set of 745 pEC50), which are accurate and satisfactory. The optimal RF model is comparable to other published QSTR models for Daphnia magna, although the optimal RF model possessed a small descriptor subset and dealt with a large dataset of pesticide toxicity pEC50. Thus, the investigation in this work provides a reliable, applicable QSTR model for predicting the toxicity pEC50 of pesticides towards Daphnia magna.

Exposure limits for indoor volatile substances concerning the general population: The role of population-based differences in sensory irritation of the eyes and airways for assessment factors

Assessment factors (AFs) are essential in the derivation of occupational exposure limits (OELs) and indoor air quality guidelines. The factors shall accommodate differences in sensitivity between subgroups, i.e., workers, healthy and sick people, and occupational exposure versus life-long exposure for the general population. Derivation of AFs itself is based on empirical knowledge from human and animal exposure studies with immanent uncertainty in the empirical evidence due to knowledge gaps and experimental reliability. Sensory irritation in the eyes and airways constitute about 30-40% of OELs and is an abundant symptom in non-industrial buildings characterizing the indoor air quality and general health. Intraspecies differences between subgroups of the general population should be quantified for the proposal of more 'empirical' based AFs. In this review, we focus on sensitivity differences in sensory irritation about gender, age, health status, and vulnerability in people, based solely on human exposure studies. Females are more sensitive to sensory irritation than males for few volatile substances. Older people appear less sensitive than younger ones. However, impaired defense mechanisms may increase vulnerability in the long term. Empirical evidence of sensory irritation in children is rare and limited to children down to the age of six years. Studies of the nervous system in children compared to adults suggest a higher sensitivity in children; however, some defense mechanisms are more efficient in children than in adults. Usually, exposure studies are performed with healthy subjects. Exposure studies with sick people are not representative due to the deselection of subjects with moderate or severe eye or airway diseases, which likely underestimates the sensitivity of the group of people with diseases. Psychological characterization like personality factors shows that concentrations of volatile substances far below their sensory irritation thresholds may influence the sensitivity, in part biased by odor perception. Thus, the protection of people with extreme personality traits is not feasible by an AF and other mitigation strategies are required. The available empirical evidence comprising age, lifestyle, and health supports an AF of not greater than up to 2 for sensory irritation. Further, general AFs are discouraged for derivation, rather substance-specific derivation of AFs is recommended based on the risk assessment of empirical data, deposition in the airways depending on the substance's water solubility and compensating for knowledge and experimental gaps. Modeling of sensory irritation would be a better 'empirical' starting point for derivation of AFs for children, older, and sick people, as human exposure studies are not possible (due to ethical reasons) or not generalizable (due to self-selection). Dedicated AFs may be derived for environments where dry air, high room temperature, and visually demanding tasks aggravate the eyes or airways than for places in which the workload is balanced, while indoor playgrounds might need other AFs due to physical workload and affected groups of the general population.

Rethinking the applicability domain analysis in QSAR models

Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.

QSAR Classification Models for Prediction of Hydroxamate Histone Deacetylase Inhibitor Activity against Malaria Parasites

Malaria, caused by Plasmodium parasites, results in >400,000 deaths annually. There is no effective vaccine, and new drugs with novel modes of action are needed because of increasing parasite resistance to current antimalarials. Histone deacetylases (HDACs) are epigenetic regulatory enzymes that catalyze post-translational protein deacetylation and are promising malaria drug targets. Here, we describe quantitative structure-activity relationship models to predict the antiplasmodial activity of hydroxamate-based HDAC inhibitors. The models incorporate P. falciparum in vitro activity data for 385 compounds containing a hydroxamic acid and were subject to internal and external validation. When used to screen 22 new hydroxamate-based HDAC inhibitors for antiplasmodial activity, model A7 (external accuracy 91%) identified three hits that were subsequently verified as having potent in vitro activity against P. falciparum parasites (IC₅₀ = 6, 71, and 84 nM), with 8 to 51-fold selectivity for P. falciparum versus human cells.

Role of medicinal plants from North Western Himalayas as an efflux pump inhibitor against MDR AcrAB-TolC Salmonella enterica serovar typhimurium: In vitro and In silico studies

ETHNOPHARMACOLOGICAL RELEVANCE

Zingiber officinale Roscoe has been utilized traditionally to cure various diseases like cold, cough, diarrhoea, nausea, asthma, vomiting, toothache, stomach upset, respiratory disorders, joint pain, and throat infection. It is also consumed as spices and ginger tea.

AIM OF THE STUDY

The current study was aimed to identify the phytocompounds of traditional medicinal plants of North-Western Himalaya that could inhibit the AcrAB-TolC efflux pump activity of Salmonella typhimurium and become sensitive to antibiotic killing at reduced dosage.

MATERIAL AND METHODS

Medicinal plant extracts were prepared using methanol, aqueous, and ethyl acetate and tested for efflux pump inhibitory activity of Salmonella typhimurium NKS70, NKS174, and NKS773 strains using Ethidium Bromide (EtBr)-agar cartwheel assay. Synergism was assessed by the agar well diffusion method and EPI activity by berberine uptake and EtBr efflux inhibition assays. Microdilution method and checkerboard assays were done to determine the minimum inhibitory concentration (MIC) and fractional inhibitory concentration index (FICI) respectively for a bioactive compound. To validate the phytocompound and efflux pump interaction, molecular docking with 6IE8 (RamA) and 6IE9 (RamR) targets was done using autoDock vina software. Toxicity prediction and drug-likeness were predicted by using ProTox-II and Molinspiration respectively.

RESULTS

Methanolic and ethyl acetate extracts of P. integerrima, O. sanctum, C. asiatica, M. charantia, Z. officinale, and W. somnifera in combination with ciprofloxacin and tetracycline showed synergistic antimicrobial activity with GIIs of 0.61-1.32 and GIIs 0.56-1.35 respectively. Methanolic extract of Z. officinal enhanced the antimicrobial potency of berberine (2 to 4-folds) and increased the EtBr accumulation. Furthermore, bioassay-guided fractionation leads to the identification of lariciresinol in ethyl acetate fraction, which decreased the MIC by 2-to 4-folds. The ΣFIC values varied from 0.30 to 0.55 with tetracycline, that indicated synergistic/additive effects. Lariciresinol also showed a good binding affinity with 6IE8 (-7.4 kcal mol-1) and 6IE9 (-8.2 kcal mol-1), which is comparable to tetracycline and chenodeoxycholic acid. Lariciresinol followed Lipinski's rule of five.

CONCLUSION

The data suggest that lariciresinol from Z. officinale could be a potential efflux pump inhibitor that could lead to effective killing of drug resistant Salmonella typhimurium at lower MIC. Molecular docking confirmed the antibacterial EPI mechanism of lariciresinol in Salmonella typhimurium and confirmed to be safe for future use.

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

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

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

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

Drug repurposing for ligand-induced rearrangement of Sirt2 active site-based inhibitors via molecular modeling and quantum mechanics calculations

Sirtuin 2 (Sirt2) nicotinamide adenine dinucleotide-dependent deacetylase enzyme has been reported to alter diverse biological functions in the cells and onset of diseases, including cancer, aging, and neurodegenerative diseases, which implicate the regulation of Sirt2 function as a potential drug target. Available Sirt2 inhibitors or modulators exhibit insufficient specificity and potency, and even partially contradictory Sirt2 effects were described for the available inhibitors. Herein, we applied computational screening and evaluation of FDA-approved drugs for highly selective modulation of Sirt2 activity via a unique inhibitory mechanism as reported earlier for SirReal2 inhibitor. Application of stringent molecular docking results in the identification of 48 FDA-approved drugs as selective putative inhibitors of Sirt2, but only top 10 drugs with docking scores > - 11 kcal/mol were considered in reference to SirReal2 inhibitor for computational analysis. The molecular dynamics simulations and post-simulation analysis of Sirt2-drug complexes revealed substantial stability for Fluphenazine and Nintedanib with Sirt2. Additionally, developed 3D-QSAR-models also support the inhibitory potential of drugs, which exclusively revealed highest activities for Nintedanib (pIC50 ≥ 5.90 µM). Conclusively, screened FDA-approved drugs were advocated as promising agents for Sirt2 inhibition and required in vitro investigation for Sirt2 targeted drug development.

Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models

In recent times, machine learning has become increasingly prominent in predictive toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro methods together with other computational methods such as quantitative structure-activity relationship modeling and absorption, distribution, metabolism, and excretion calculations are being used. An overview of machine learning and its applications in predictive toxicology is presented here, including support vector machines (SVMs), random forest (RF) and decision trees (DTs), neural networks, regression models, naïve Bayes, k-nearest neighbors, and ensemble learning. The recent successes of these machine learning methods in predictive toxicology are summarized, and a comparison of some models used in predictive toxicology is presented. In predictive toxicology, SVMs, RF, and DTs are the dominant machine learning methods due to the characteristics of the data available. Lastly, this review describes the current challenges facing the use of machine learning in predictive toxicology and offers insights into the possible areas of improvement in the field.

QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer's Disease

Alzheimer’s disease is one of the most common neurodegenerative disorders in elder population. The β-site amyloid cleavage enzyme 1 (BACE1) is the major constituent of amyloid plaques and plays a central role in this brain pathogenesis, thus it constitutes an auspicious pharmacological target for its treatment. In this paper, a QSAR model for identification of potential inhibitors of BACE1 protein is designed by using classification methods. For building this model, a database with 215 molecules collected from different sources has been assembled. This dataset contains diverse compounds with different scaffolds and physical-chemical properties, covering a wide chemical space in the drug-like range. The most distinctive aspect of the applied QSAR strategy is the combination of hybridization with backward elimination of models, which contributes to improve the quality of the final QSAR model. Another relevant step is the visual analysis of the molecular descriptors that allows guaranteeing the absence of information redundancy in the model. The QSAR model performances have been assessed by traditional metrics, and the final proposed model has low cardinality, and reaches a high percentage of chemical compounds correctly classified.

A QSAR Study for Analgesic and Anti-inflammatory Activities of 5-/6-Acyl-3-alkyl-2-Benzoxazolinone Derivatives

In this publication, QSAR models were developed to predict analgesic and anti-inflammatory activities of some 2-benzoxazolinone derivatives using multiple linear regression method. The models were validated internally and externally according to the OECD principles. With the help of these models, pronounced molecular properties of these compounds related to activities were also explored. The developed models demonstrated that hydrophobicity, the number of halogens, and the shape of the molecular structure of these candidate drugs are prominent to represent analgesic and anti-inflammatory activities. Based on the previously tested compounds and the developed models, 77 new compounds were designed as potential analgesic and anti-inflammatory drugs. Majority of the newly designed compounds demonstrated promising analgesic and anti-inflammatory activity.

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

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

Cypermethrin induced toxicities in fish and adverse health outcomes: Its prevention and control measure adaptation

Pesticides are being widely employed in the modern agriculture, though in different quantities, across the globe. Although it is useful for crops yield enhancement, however, there are the serious environment, health and safety related concerns for aquatic and terrestrial living biomes that include humans, animals, and plants. Various in practice and emerging pesticides adversely affect the survival, development and biological systems stability. Several research efforts have been made to highlight the bio-safety and toxicological features of toxicants through risk assessment studies using different animal models, e.g., different fish species. Among several pesticides, cypermethrin is extensively used in agriculture and households, and the reported concentrations of this pesticide in different water bodies including rivers and streams, soil and even in rainwater are threatening. Consequently, cypermethrin is considered for risk assessment studies to know about its deep and different level of toxicological effects subject to its dose, exposure time and route. The cypermethrin existence/persistence in the environment is posing a severe threat to humans as well as another non-target terrestrial and aquatic organism. Herein, the toxic effects of pesticides, with special reference to cypermethrin, on fish, the mode of toxicity, concerns regarding public health and harmful impacts on human beings are comprehensively reviewed. The information is also given on their appropriate control and prevention strategies.

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.

Improving confidence in (Q)SAR predictions under Canada's Chemicals Management Plan - a chemical space approach

One of the key challenges of Canada's Chemicals Management Plan (CMP) is assessing chemicals with limited/no empirical hazard data for their risk to human health. In some instances, these chemicals have not been tested broadly for their toxicological potency; as such, limited information exists on their potential to induce human health effects following exposure. Although (quantitative) structure activity relationship ((Q)SAR) models are able to generate predictions to address data gaps for certain toxicological endpoints, the confidence in predictions also needs to be addressed. One way to address this issue is to apply a chemical space approach. This approach uses international toxicological databases, for example, those available in the Organisation for Economic Co-operation and Development (OECD) QSAR Toolbox. The approach,assesses a model's ability to predict the potential hazards of chemicals that have limited hazard data that require assessment under the CMP when compared to a larger, data-rich chemical space that is structurally similar to chemicals of interest. This evaluation of a model's predictive ability makes (Q)SAR analysis more transparent and increases confidence in the application of these predictions in a risk-assessment context. Using this approach, predictions for such chemicals obtained from four (Q)SAR models were successfully classified into high, medium and low confidence levels to better inform their use in decision-making.

A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, domain of application and prediction

Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides.

Large-Scale Predictive Drug Safety: From Structural Alerts to Biological Mechanisms

The recent explosion of data linking drugs, proteins, and pathways with safety events has promoted the development of integrative systems approaches to large-scale predictive drug safety. The added value of such approaches is that, beyond the traditional identification of potentially labile chemical fragments for selected toxicity end points, they have the potential to provide mechanistic insights for a much larger and diverse set of safety events in a statistically sound nonsupervised manner, based on the similarity to drug classes, the interaction with secondary targets, and the interference with biological pathways. The combined identification of chemical and biological hazards enhances our ability to assess the safety risk of bioactive small molecules with higher confidence than that using structural alerts only. We are still a very long way from reliably predicting drug safety, but advances toward gaining a better understanding of the mechanisms leading to adverse outcomes represent a step forward in this direction.

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

The adverse effects to humans and environment of only few chemicals are well known. Absorption, distribution, metabolism, and excretion (ADME) are the steps of pharmaco/toxicokinetics that determine the internal dose of chemicals to which the organism is exposed. Of all the xenobiotic-metabolizing enzymes, the cytochrome P450 (CYP) enzymes are the most important due to their abundance and versatility. Reactions catalyzed by CYPs usually turn xenobiotics to harmless and excretable metabolites, but sometimes an innocuous xenobiotic is transformed into a toxic metabolite. Data on ADME and toxicity properties of compounds are increasingly generated using in vitro and modeling (in silico) tools. Both physics-based and empirical modeling approaches are used. Numerous ligand-based and target-based as well as combined modeling methods have been employed to evaluate determinants of CYP ligand binding as well as predicting sites of metabolism and inhibition characteristics of test molecules. In silico prediction of CYP–ligand interactions have made crucial contributions in understanding (1) determinants of CYP ligand binding recognition and affinity; (2) prediction of likely metabolites from substrates; (3) prediction of inhibitors and their inhibition potency. Truly predictive models of toxic outcomes cannot be created without incorporating metabolic characteristics; in silico methods help producing such information and filling gaps in experimentally derived data. Currently modeling methods are not mature enough to replace standard in vitro and in vivo approaches, but they are already used as an important component in risk assessment of drugs and other chemicals.

An Introduction to the Basic Concepts in QSAR-Aided Drug Design

The need for the development of new drugs to combat existing and newly identified conditions is unavoidable. One of the important tools used in the advanced drug development pipeline is computer-aided drug design. Traditionally, to find a drug many ligands were synthesized and evaluated for their effectiveness using suitable bioassays and if all other drug-likeness features were met, the candidate(s) would possibly reach the market. Although this approach is still in use in advanced format, computational methods are an indispensable component of modern drug development projects. One of the methods used from very early days of rationalizing the drug design approaches is Quantitative Structure-Activity Relationship (QSAR). This chapter overviews QSAR modeling steps by introducing molecular descriptors, mathematical model development for relating biological activities to molecular structures, and model validation. At the end, several successful cases where QSAR studies were used extensively are presented.