Tool For Drug Design Research Articles

Advancements in drug discovery: integrating CADD tools and drug repurposing for PD-1/PD-L1 axis inhibition.

Despite significant strides in improving cancer survival rates, the global cancer burden remains substantial, with an anticipated rise in new cases. Immune checkpoints, key regulators of immune responses, play a crucial role in cancer evasion mechanisms. The discovery of immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 has revolutionized cancer treatment, with monoclonal antibodies (mAbs) becoming widely prescribed. However, challenges with current mAb ICIs, such as limited oral bioavailability, adverse effects, and high costs, underscore the need to explore alternative small-molecule inhibitors. In this work, we aimed to identify new potential ICI among all FDA-approved drugs. We employed QSAR models to predict PD-1/PD-L1 inhibition, utilizing a diverse dataset of 29 197 molecules sourced from ChEMBL, PubChem, and recent literature. Machine learning techniques, including Random Forest, Support Vector Machine, and Convolutional Neural Network, were employed for benchmarking to assess model performance. Additionally, we undertook a drug repurposing strategy, leveraging the best in silico model for a virtual screening campaign involving 1576 off-patent approved drugs. Only two virtual screening hits were proposed based on the criteria established for this approach, including: (1) QSAR probability of being active against PD-L1; (2) QSAR applicability domain; (3) prediction of the affinity between the PD-L1 and ligands through molecular docking. One of the proposed hits was sonidegib, an anticancer drug, featuring a biphenyl system. Sonidegib was subsequently validated for in vitro PD-1/PD-L1 binding modulation using ELISA and flow cytometry. This integrated approach, which combines computer-aided drug design (CADD) tools, QSAR modelling, drug repurposing, and molecular docking, offers a pioneering strategy to expedite drug discovery for PD-1/PD-L1 axis inhibition. The findings underscore the potential to identify a wider range small molecules to contribute to the ongoing efforts to advancing cancer immunotherapy.

Artificial Intelligence in Computer-Aided Drug Design (CADD) Tools for the Finding of Potent Biologically Active Small Molecules: Traditional to Modern Approach.

Computer-Aided Drug Design (CADD) entails designing molecules that could potentially interact with a specific biomolecular target and promising their potential binding. The stereo- arrangement and stereo-selectivity of small molecules (SMs)--based chemotherapeutic agents significantly influence their therapeutic potential and enhance their therapeutic advantages. CADD has been a well-established field for decades, but recent years have observed a significant shift toward acceptance of computational approaches in both academia and the pharmaceutical industry. Recently, artificial intelligence (AI), bioinformatics, and data science have played a significant role in drug discovery to accelerate the development of effective treatments, reduce expenses, and eliminate the need for animal testing. This shift can be attributed to the availability of extensive data on molecular properties, binding to therapeutic targets, and their 3D structures. Increasing interest from legislators, pharmaceutical companies, and academic and industrial scientists is evidence that AI is reshaping the drug discovery industry. To achieve success in drug discovery, it is necessary to optimize pharmacodynamic, pharmacokinetic, and clinical outcome-related properties. Moreover, the advent of on-demand virtual libraries containing billions of drug-like SMs, coupled with abundant computing capacities, has further facilitated this transition. To fully capitalize on these resources, rapid computational methods are needed for effective ligand screening. This includes structure-based virtual screening (SBVS) of vast chemical spaces, aided by fast iterative screening approaches. At the same time, advances in deep learning (DL) predictions of ligand properties and target activities have become very helpful, as they no longer need information about the structure of the receptor. This study examines recent progress in the drug discovery and development (DDD) approach, their potential to reshape the entire DDD process, and the challenges they face. This review examines the role of artificial intelligence as a fundamental component in drug discovery, particularly focusing on small molecules. It also discusses how AI-driven approaches can expedite the identification of diverse, potent, target-specific, and drug-like ligands for protein targets. This advancement has the potential to make drug discovery more efficient and cost-effective, ultimately facilitating the development of safer and more effective therapeutics.

Volatile Organic Compound–Drug Receptor Interactions: A Potential Tool for Drug Design in the Search for Remedies for Increasing Toxic Occupational Exposure

Volatile Organic Compound–Drug Receptor Interactions: A Potential Tool for Drug Design in the Search for Remedies for Increasing Toxic Occupational Exposure

Multiple Binding Modes of Inhibitors to Human Carbonic Anhydrases: An Update on the Design of Isoform-Specific Modulators of Activity.

Human carbonic anhydrases (hCAs) are widespread zinc enzymes that catalyze the hydration of CO2 to bicarbonate and a proton. Currently, 15 isoforms have been identified, of which only 12 are catalytically active. Given their involvement in numerous physiological and pathological processes, hCAs are recognized therapeutic targets for the development of inhibitors with biomedical applications. However, despite massive development efforts, very few of the presently available hCA inhibitors show selectivity for a specific isoform. X-ray crystallography is a very useful tool for the rational drug design of enzyme inhibitors. In 2012 we published in Chemical Reviews a highly cited review on hCA family (Alterio, V. et al. Chem Rev. 2012, 112, 4421-4468), analyzing about 300 crystallographic structures of hCA/inhibitor complexes and describing the different CA inhibition mechanisms existing up to that date. However, in the period 2012-2023, almost 700 new hCA/inhibitor complex structures have been deposited in the PDB and a large number of new inhibitor classes have been discovered. Based on these considerations, the aim of this Review is to give a comprehensive update of the structural aspects of hCA/inhibitor interactions covering the period 2012-2023 and to recapitulate how this information can be used for the rational design of more selective versions of such inhibitors.

Structure-based virtual screening of Trachyspermum ammi metabolites targeting acetylcholinesterase for Alzheimer's disease treatment.

In recent decades, Alzheimer's disease (AD) has garnered significant attention due to its rapid global prevalence. The cholinergic hypothesis posits that the degradation of acetylcholine by acetylcholinesterase (AChE) contributes to AD development. Despite existing anti-AChE drugs, their adverse side effects necessitate new agents. This study analyzed 150 bioactive phytochemicals from Trachyspermum ammi using structure-based drug design and various in-silico tools to identify potent anti-AChE compounds. Compounds were screened for drug-likeness (QEDw ≥50%) and bioavailability (≥55%) and underwent toxicity profiling via the ProTox-II server. Selected compounds were prepared for molecular docking with the human AChE protein as the receptor. Viridifloral, 2-Methyl-3-glucosyloxy-5-isopropyl phenol, Alpha-Curcumene, and Sterol emerged as top candidates with high AChE affinity. These results were validated by molecular dynamics simulations, confirming stable interactions. The hit compounds were further evaluated for drug-likeness using Lipinski's rule and ADMET properties, confirming favorable pharmacokinetic profiles. DFT optimization analyzed frontier molecular orbitals and electrostatic potential, demonstrating favorable chemical reactivity and stability. This study suggests that these identified compounds could be novel nature-derived AChE inhibitors, potentially contributing to AD treatment. However, further in-vitro and in-vivo studies are necessary to confirm their efficacy in biological systems. Future research will focus on developing these compounds into safe and effective drugs to combat Alzheimer's disease.

Latin American Natural Product Database (LANaPDB): An Update.

Natural product (NP) databases are crucial tools in computer-aided drug design (CADD). Over the past decade, there has been a worldwide effort to assemble information regarding natural products (NPs) isolated and characterized in certain geographical regions. In 2023, it was published LANaPDB, and to our knowledge, this is the first attempt to gather and standardize all the NP databases of Latin America. Herein, we present and analyze in detail the contents of an updated version of LANaPDB, which includes 619 newly added compounds from Colombia, Costa Rica, and Mexico. The present version of LANaPDB has a total of 13 578 compounds, coming from ten databases of seven Latin American countries. A chemoinformatic characterization of LANaPDB was carried out, which includes the structural classification of the compounds, calculation of six physicochemical properties of pharmaceutical interest, and visualization of the chemical space by employing and comparing two different fingerprints (MACCS keys (166-bit) and Morgan2 (2048-bit)). Furthermore, additional analyses were made, and valuable information not included in the first version of LANaPDB was added, which includes structural diversity, molecular complexity, synthetic feasibility, commercial availability, and reported and predicted biological activity. In addition, the LANaPDB compounds were cross-referenced to two of the largest public chemical compound databases annotated with biological activity: ChEMBL and PubChem.

Recent Progress for the Synthesis of Pyrrole Derivatives – An Update

Abstract: Pyrrole is a versatile heterocyclic moiety exhibiting a wide range of pharmacological actions with high therapeutic value. The importance of pyrrole in the pharmaceutical field lies in its versatility, selectivity, and biocompatibility, and these properties make it a valuable tool for drug design and development. The pyrrole moiety is a fundamental building block for many biologically active molecules and has gathered significant attention in the fields of medicinal and organic chemistry; hence, its synthesis has been a crucial area for research. There are various conventional as well as modern approaches to acquiring a series of pyrrole scaffolds, with a wide range of attractive features and drawbacks pertaining to each approach. An extensive amount of literature must be studied to compare the best synthetic routes. This article highlights the applications of pyrrole derivatives in various fields, such as drug discovery, material science, and catalysis, and provides an overview of modern synthetic pathways that include metals, nanomaterials, and complex heterogeneous catalysed methods for pyrrole derivatives. Special emphasis is given to the use of green chemistry principles like green solvent-based methods, microwave-aided methods, and solvent-free methods in the synthesis of pyrroles, with the recent developments and prospects in the synthetic and organic chemistry fields. Overall, this review article provides a comprehensive overview of the synthesis of pyrroles and complies with all the possible developments in the synthetic routes for pyrroles within 2015– 2022. Among all, the reactions catalysed by proline, copper oxides, and oxones have been shown to be the most effective synthetic route for pyrrole derivatives at mild reaction conditions and with excellent yields. This information will be helpful for researchers interested in the development of new pyrrole-based compounds. The categorization in this review provides an easy means for the reader to rationally select the best possible synthetic method for pyrrole derivatives.

OpenDock: a pytorch-based open-source framework for protein-ligand docking and modelling.

Molecular docking is an invaluable computational tool with broad applications in computer-aided drug design and enzyme engineering. However, current molecular docking tools are typically implemented in languages such as C++ for calculation speed, which lack flexibility and user-friendliness for further development. Moreover, validating the effectiveness of external scoring functions for molecular docking and screening within these frameworks is challenging, and implementing more efficient sampling strategies is not straightforward. To address these limitations, we have developed an open-source molecular docking framework, OpenDock, based on Python and PyTorch. This framework supports the integration of multiple scoring functions; some can be utilized during molecular docking and pose optimization, while others can be used for post-processing scoring. In terms of sampling, the current version of this framework supports simulated annealing and Monte Carlo optimization. Additionally, it can be extended to include methods such as genetic algorithms and particle swarm optimization for sampling docking poses and protein side chain orientations. Distance constraints are also implemented to enable covalent docking, restricted docking or distance map constraints guided pose sampling. Overall, this framework serves as a valuable tool in drug design and enzyme engineering, offering significant flexibility for most protein-ligand modelling tasks. OpenDock is publicly available at: https://github.com/guyuehuo/opendock.

Topological coindices and QSPR analysis for some potential drugs used in lung cancer treatment via CoM and CoNM-polynomials

Topological indices are used to convert a chemical structure into a real number, usually to study the physicochemical and biological properties of molecules. The groundwork is prepared for the interpretation of the obtained data by processing with Quantitative Structure Property/Activity Relationship (QSPR/QSAR). In this study, the drugs lorlatinib, gefitinib, sotorasib, pralsetinib, crizotinib, adagrasib, alectinib, brigatinib, dacomitinib and entrectinib, which are potential to be used in the treatment of lung cancer, are discussed. Topological coindices are calculated with the help of CoM and CoNM polynomials obtained with the graph structures of these drugs. The relationship between topological coindices and physicochemical properties such as evaporation enthalpy, flash point, molar refraction, polarisation, surface tension, molar volume are investigated by QSPR analysis. At this stage, linear, logarithmic and quadratic regression methods have been used. The results show that the values of these topological indices are highly correlated with certain physicochemical properties of the used some drugs in the treatment of lung cancer. In addition, using comparative analysis, the actual values and the values calculated with the help of topological indices have been examined in terms of predictive ability. The findings of this search demonstrate topological indices’ potential as tools for cancer drug discovery and design.

An Overview of Molecular Docking

The computational modeling of structural complexes formed by two or more interacting molecules is known as molecular docking. Prediction of an interesting three-dimensional structure is the primary goal of molecular docking. Software for molecular docking is mostly employed in the development of drugs. Molecules and the simple use of structural databases caused damage to an important mechanism. Several expensive tools for drug design and research are provided by molecular docking. Simple molecular prediction as well as rapid access to structural databases have become important components on the medicinal chemist's desktop. Virtual screening is the most important contribution of molecular docking. Numerous docking programs were used to visualize the three-dimensional structure of the molecule, and different computational techniques can be used to analyze docking gain. In structural molecular biology and computer-aided drug design, molecular docking is a key tool. Docking is useful for lead optimization because it can be used to do virtual screening on huge collections of compounds, rate the results, and provide structural ideas for how the ligands affect the target.

Prediction of mutation-induced protein stability changes based on the geometric representations learned by a self-supervised method

BackgroundThermostability is a fundamental property of proteins to maintain their biological functions. Predicting protein stability changes upon mutation is important for our understanding protein structure–function relationship, and is also of great interest in protein engineering and pharmaceutical design.ResultsHere we present mutDDG-SSM, a deep learning-based framework that uses the geometric representations encoded in protein structure to predict the mutation-induced protein stability changes. mutDDG-SSM consists of two parts: a graph attention network-based protein structural feature extractor that is trained with a self-supervised learning scheme using large-scale high-resolution protein structures, and an eXtreme Gradient Boosting model-based stability change predictor with an advantage of alleviating overfitting problem. The performance of mutDDG-SSM was tested on several widely-used independent datasets. Then, myoglobin and p53 were used as case studies to illustrate the effectiveness of the model in predicting protein stability changes upon mutations. Our results show that mutDDG-SSM achieved high performance in estimating the effects of mutations on protein stability. In addition, mutDDG-SSM exhibited good unbiasedness, where the prediction accuracy on the inverse mutations is as well as that on the direct mutations.ConclusionMeaningful features can be extracted from our pre-trained model to build downstream tasks and our model may serve as a valuable tool for protein engineering and drug design.

SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening.

The growing interest in RNA-targeted drugs underscores the need for computational modeling of interactions between RNA molecules and small compounds. Having a reliable scoring function for RNA-ligand interactions is essential for effective computational drug screening. An ideal scoring function should not only predict the native pose for ligand binding but also rank the affinity of the binding for different ligands. However, existing scoring functions are primarily designed to predict the native binding modes for a given RNA-ligand pair and have not been thoroughly assessed for virtual screening purposes. In this paper, we introduce SPRank, a combination of machine-learning and knowledge-based scoring functions developed through a weighted iterative approach, specifically designed to tackle both binding mode prediction and virtual screening challenges. Our approach incorporates third-party docking software, such as rDock and AutoDock Vina, to sample flexible ligands against an ensemble of RNA structures, capturing the conformational flexibility of both the RNA and the ligand. Through rigorous testing, SPRank demonstrates improved performance compared to the tested scoring functions across four test sets comprising 122, 42, 55, and 71 nucleic acid-ligand complexes. Furthermore, SPRank exhibits improved performance in virtual screening tests targeting the HIV-1 TAR ensemble, which highlights its advantage in drug discovery. These results underscore the advantages of SPRank as a potentially promising tool for the RNA-targeted drug design. The source code of SPRank and the data sets are freely accessible at https://github.com/Vfold-RNA/SPRank.

Nanobody against SARS-CoV-2 non-structural protein Nsp9 inhibits viral replication in human airway epithelia

Nanobodies are emerging as critical tools for drug design. Several have been recently created to serve as inhibitors of SARS-CoV-2 entry in the host cell by targeting surface-exposed Spike protein. Here we have established a pipeline that instead targets highly conserved viral proteins made only after viral entry into the host cell when the SARS-CoV-2 RNA-based genome is translated. As proof of principle, we designed nanobodies against the SARS-CoV-2 non-structural protein Nsp9, required for viral genome replication. One of these anti-Nsp9 nanobodies, 2NSP23, previously characterized using immunoassays and NMR spectroscopy for epitope mapping, was expressed and found to block SARS-CoV-2 replication specifically. We next encapsulated 2NSP23 nanobody into lipid nanoparticles (LNP) as mRNA. We show that this nanobody, hereby referred to as LNP-mRNA-2NSP23, is internalized and translated in cells and suppresses multiple SARS-CoV-2 variants as seen by qPCR and RNA deep sequencing. These results are corroborated in 3D reconstituted human epithelium kept at air/liquid interface to mimic the outer surface of lung tissue. These observations indicate that LNP-mRNA-2NSP23 is internalized and following translation, it inhibits viral replication by targeting Nsp9 in living cells. We speculate that LNP-mRNA-2NSP23 may be translated into an innovative strategy to generate novel antiviral drugs highly efficient across coronaviruses.

Unveiling novel insights into human IL-6 − IL-6R interaction sites through 3D computer-guided docking and systematic site mutagenesis

The cytokine interleukin-6 (IL-6) plays a crucial role in autoimmune and inflammatory diseases. Understanding the precise mechanism of IL-6 interaction at the amino acid level is essential to develop IL-6-inhibiting compounds. In this study, we employed computer-guided drug design tools to predict the key residues that are involved in the interaction between IL-6 and its receptor IL-6R. Subsequently, we generated IL-6 mutants and evaluated their binding affinity to IL-6R and the IL-6R − gp130 complex, as well as monitoring their biological activities. Our findings revealed that the R167A mutant exhibited increased affinity for IL-6R, leading to enhanced binding to IL-6R − gp130 complex and subsequently elevated intracellular phosphorylation of STAT3 in effector cells. On the other hand, although E171A reduced its affinity for IL-6R, it displayed stronger binding to the IL-6R − gp130 complex, thereby enhancing its biological activity. Furthermore, we identified the importance of R178 and R181 for the precise recognition of IL-6 by IL-6R. Mutants R181A/V failed to bind to IL-6R, while maintaining an affinity for the IL-6 − gp130 complex. Additionally, deletion of the D helix resulted in complete loss of IL-6 binding affinity for IL-6R. Overall, this study provides valuable insights into the binding mechanism of IL-6 and establishes a solid foundation for future design of novel IL-6 inhibitors.

ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery.

In recent years, reinforcement learning (RL) has emerged as a valuable tool in drug design, offering the potential to propose and optimize molecules with desired properties. However, striking a balance between capabilities, flexibility, reliability, and efficiency remains challenging due to the complexity of advanced RL algorithms and the significant reliance on specialized code. In this work, we introduce ACEGEN, a comprehensive and streamlined toolkit tailored for generative drug design, built using TorchRL, a modern RL library that offers thoroughly tested reusable components. We validate ACEGEN by benchmarking against other published generative modeling algorithms and show comparable or improved performance. We also show examples of ACEGEN applied in multiple drug discovery case studies. ACEGEN is accessible at https://github.com/acellera/acegen-open and available for use under the MIT license.

Computational Approaches: A New Frontier in Cancer Research.

Cancer is a broad category of disease that can start in virtually any organ or tissue of the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to the most recent statistics, cancer will be the cause of 10 million deaths worldwide in 2020, accounting for one death out of every six worldwide. The typical approach used in anti-cancer research is highly time-consuming and expensive, and the outcomes are not particularly encouraging. Computational techniques have been employed in anti-cancer research to advance our understanding. Recent years have seen a significant and exceptional impact on anticancer research due to the rapid development of computational tools for novel drug discovery, drug design, genetic studies, genome characterization, cancer imaging and detection, radiotherapy, cancer metabolomics, and novel therapeutic approaches. In this paper, we examined the various subfields of contemporary computational techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, and QSAR, and their applications in the study of cancer.

PHCDTI: A multichannel parallel high-order feature crossover model for DTIs prediction

The exploration of non-covalent interactions between drugs and proteins (NCIs) has significantly improved the performance of drug–target interactions (DTIs) prediction models. However, existing methods for simulating NCIs are limited to single or multiple interactions, ignoring the combinatorial effects (i.e., high-order crossover between drugs and proteins (HOC-D&P)) that can arise when multiple NCIs coexist. To overcome this limitation, a multi-channel parallel high-order prediction model (PHCDTI) is proposed, aiming to simulate the complex interactions between drugs and proteins, thereby modeling HOC-D&P. Firstly, a tri-channel parallel model structure is constructed, allowing each channel to independently learn the interaction patterns between drug molecules and amino acid groups. Secondly, the three channels are designed from low-order to high-order, incorporating linear, inner-product, and pair-interaction feature crossover methods to simulate the complex interactions between drug molecules and amino acid groups. The stacking of residual connections enables the model to effectively model HOC-D&P. Additionally, by utilizing multi-head attention mechanisms for inner product and squeeze-excitation networks (SENET) for pair-interaction, the interpretability of the NCIs modeling process and the specific representation of drug molecules or amino acid groups are achieved. PHCDTI was evaluated on three benchmark datasets in four experimental settings, and the results indicated that it outperformed the most recent baseline model. In all cases, the average accuracy, precision, recall, AUC, and AUPR of PHCDTI are improved over the best baseline model by 3.56%, 6.06%, 3.99%, 2.50%, and 8.11%, respectively. Moreover, strong information filtering capabilities were exhibited by PHCDTI, with the effectiveness of the three-channel design confirmed by ablation studies. Finally, a case study on the human γ-aminobutyric acid receptors (GABARs) demonstrated that PHCDTI can serve as a powerful tool for real-world drug screening and design.

Modified heated CGMD simulations for discovering stable docked conformations of BiTE antibody against CD3 and CD117/c-kit

ABSTRACT In cancer immunotherapy, the design and optimisation of bispecific antibodies hold great promise. Bispecific T-cell engager (BiTE) antibodies targeting CD3 and CD117/c-kit have shown significant potential in experimental settings. Nevertheless, knowledge on their stable docked conformations at molecular level is still limited. This study presents an approach of employing modified heated coarse-grained molecular dynamics (CGMD) simulations to elucidate the stable docked conformations of BiTE antibodies against CD3 and CD117/c-kit. We integrated molecular dynamics simulation with coarse-grained and temperature control to explore the conformational landscape of these complex interactions. The modified heated CGMD simulation aimed to re-assess the docked poses suggested by ClusPro webserver. Furthermore, the all-atomic trajectories unveiled the dynamic residues formed throughout the simulation process. The per-residue-energy-binding emphasised the crucial amino acids involved in binding within the complex especially between the complementarity-determining regions (CDR) of BiTEs and residues located at the N-terminal of CD117/c-kit and the C-terminal of CD3. The formation of three types of interactions, such as hydrogen bonds, salt-bridge contact and hydrophobic interactions plays a crucial role in the motion, configuration and the free energy landscape of the complexes. This method is a valuable tool for rational drug design especially in the field of cancer immunotherapy.

Redefining a new frontier in alkaptonuria therapy with AI-driven drug candidate design via in- silico innovation.

A rare metabolic condition called alkaptonuria (AKU) is caused by a decrease in homogentisate 1,2 dioxygenase (HGO) activity due to a mutation in homogentisate dioxygenase (HGD) gene. Homogentisic acid is a byproduct of the catabolism of tyrosine and phenylalanine that darkens the urine and accumulates in connective tissues which causes an agonizing arthritis. Employing the use of deep learning artificial intelligence (AI) drug design, this study aims to alleviate the current toxicity of the AKU drugs currently inuse, particularly nitisinone, by utilizing the natural flavanolkaempferol molecule as a 4-hydroxyphenylpyruvate dioxygenase inhibitor. Kaempferol was employed to generate three effective de novo drug candidates targeting the enzyme4-hydroxyphenylpyruvate dioxygenase using an AI drug design tool. We present novel AIK formulations in the present study. The AIK's (Artificial Intelligence Kaempferol) examination of drug-likeliness among the three led to its choice as a possible target. The toxicity assessment research ofAIK demonstrates that it is not only safer to use than othertreatments, but also more efficient. The docking of theAIGT with 4-hydroxyphenylpyruvate dioxygenase, whichrevealed a binding affinity of around-9.099 kcal/mol, highlights the AIK's potential as a therapeutic candidate. Aninnovative approach to deal with challenging circumstances is thus presented in this study by new formulations kaempferol that have been meticulously designed by AI. The results of the invitro tests must be confirmed invivo, even though AI-designed AIK is effective and sufficiently safe as computed.

The role of physicochemical and topological parameters in drug design

Quantitative structure activity relationship (QSAR) is a widely used tool in rational drug design that establishes relationships between the physicochemical and topological descriptors of ligands and their biological activities. Obtained QSAR models help identify descriptors that play pivotal roles in the biological activity of ligands. This not only helps the prediction of new compounds with desirable biological activities but also helps with the design of new compounds with better activities and low toxicities. QSAR commonly uses lipophilicity (logP), hydrophobicity (logD), water solubility (logS), the acid–base dissociation constant (pKa), the dipole moment, the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), molecular weight (MW), molar volume (MV), molar refractivity (MR), and the kappa index as physicochemical parameters. Some commonly used topological indices in QSAR are the Wiener index, Platt index, Hosoya index, Zagreb indices, Balaban index, and E-state index. This review presents a brief description of the significance of the most extensively used physicochemical and topological parameters in drug design.