Conventional Drug Design Research Articles

Exploitation of Proximity-Mediated Effects in Drug Discovery: An Update of Recent Research Highlights in Perturbing Pathogenic Proteins and Correlated Issues.

The utilization of proximity-mediated effects to perturb pathogenic proteins of interest (POIs) has emerged as a powerful strategic alternative to conventional drug design approaches based on target occupancy. Over the past three years, the burgeoning field of targeted protein degradation (TPD) has witnessed the expansion of degradable POIs to membrane-associated, extracellular, proteasome-resistant, and even microbial proteins. Beyond TPD, researchers have achieved the proximity-mediated targeted protein stabilization, the recruitment of intracellular immunophilins to disturb undruggable targets, and the nonphysiological post-translational modifications of POIs. All of these strides provide new avenues for innovative drug discovery aimed at battling human malignancies and other major diseases. This perspective presents recent research highlights and discusses correlated issues in developing therapeutic modalities that exploit proximity-mediated effects to modulate pathogenic proteins, thereby guiding future academic and industrial efforts in this field.

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry.

De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. The drug development process is time- and resource-consuming, and it has a low probability of success. Recent advances in machine learning and deep learning technology have reduced the time and cost of the discovery process and therefore, improved pharmaceutical research and development. In this paper, we explore the combination of two rapidly developing fields with lead candidate discovery in the drug development process. First, artificial intelligence has already been demonstrated to successfully accelerate conventional drug design approaches. Second, quantum computing has demonstrated promising potential in different applications, such as quantum chemistry, combinatorial optimizations, and machine learning. This article explores hybrid quantum-classical generative adversarial networks (GAN) for small molecule discovery. We substituted each element of GAN with a variational quantum circuit (VQC) and demonstrated the quantum advantages in the small drug discovery. Utilizing a VQC in the noise generator of a GAN to generate small molecules achieves better physicochemical properties and performance in the goal-directed benchmark than the classical counterpart. Moreover, we demonstrate the potential of a VQC with only tens of learnable parameters in the generator of GAN to generate small molecules. We also demonstrate the quantum advantage of a VQC in the discriminator of GAN. In this hybrid model, the number of learnable parameters is significantly less than the classical ones, and it can still generate valid molecules. The hybrid model with only tens of training parameters in the quantum discriminator outperforms the MLP-based one in terms of both generated molecule properties and the achieved KL divergence. However, the hybrid quantum-classical GANs still face challenges in generating unique and valid molecules compared to their classical counterparts.

Implementation of Ensemble Methods on Classification of CDK2 Inhibitor as Anti-Cancer Agent

Cancer is known as the second leading cause of death worldwide. About 7-10 million cases of death by cancer occur every year. The recent treatment to heal the cancer is chemotherapy. However, chemotherapy treatment is known to have side effects and cell resistance issues to certain drugs. Therefore, it is required to develop a new drug that can reduce the side effects and provide a better treatment effect. In general, anti-cancer drugs are developed by targeting Cyclin-Dependent Kinase 2 (CDK2) enzyme. Conventional drug design is not effective and efficient for obtaining new drug candidates because of no information about the biological activity before it is synthesized. In this study, we aim to develop a model to predict the activity of CDK2 inhibitors by using ensemble methods, i.e., XGBoost, Random Forest, and AdaBoost. The study was conducted by calculating several fingerprints, i.e., Estate, Extended, Maccs, and Pubchem, as feature variables. Based on the results, we found that Random Forest with Pubchem fingerprint gives the best result with the value of Matthews Correlation Coefficient (MCC) and Area Under the ROC Curve (AUC) values are 0.979 and 0.999, respectively. From this study, we contributed to revealing the potency of the ensemble with fingerprint in bioactivity prediction, especially CDK2 inhibitors as anti-cancer agents.

Molecular Basis of Small-Molecule Binding to α-Synuclein.

Intrinsically disordered proteins (IDPs) are implicated in many human diseases. They have generally not been amenable to conventional structure-based drug design, however, because their intrinsic conformational variability has precluded an atomic-level understanding of their binding to small molecules. Here we present long-time-scale, atomic-level molecular dynamics (MD) simulations of monomeric α-synuclein (an IDP whose aggregation is associated with Parkinson’s disease) binding the small-molecule drug fasudil in which the observed protein–ligand interactions were found to be in good agreement with previously reported NMR chemical shift data. In our simulations, fasudil, when bound, favored certain charge–charge and π-stacking interactions near the C terminus of α-synuclein but tended not to form these interactions simultaneously, rather breaking one of these interactions and forming another nearby (a mechanism we term dynamic shuttling). Further simulations with small molecules chosen to modify these interactions yielded binding affinities and key structural features of binding consistent with subsequent NMR experiments, suggesting the potential for MD-based strategies to facilitate the rational design of small molecules that bind with disordered proteins.

Zoning in on Tankyrases: A Brief Review on the Past, Present and Prospective Studies.

Tankyrases are known for their multifunctionalities within the poly(ADPribose) polymerases family and playing vital roles in various cellular processes which include the regulation of tumour suppressors. Tankyrases, which exist in two isoforms; Tankyrase 1 and 2, are highly homologous and an integral part of the Wnt β -catenin pathway that becomes overly dysregulated when hijacked by pro-carcinogenic machineries. In this review, we cover the distinct roles of the Tankyrase isoforms and their involvement in the disease pathogenesis. Also, we provide updates on experimentally and computationally derived antagonists of Tankyrase whilst highlighting the precedence of integrative computer-aided drug design methods towards the discovery of selective inhibitors. Despite the high prospects embedded in the therapeutic targeting and blockade of Tankyrase isoforms, the inability of small molecule inhibitors to achieve selective targeting has remained a major setback, even until date. This explains numerous incessant drug design efforts geared towards the development of highly selective inhibitors of the respective Tankyrase isoforms since they mediate distinct aberrancies in disease progression. Therefore, considering the setbacks of conventional drug design methods, can computer-aided approaches actually save the day? The implementation of computer-aided drug design techniques in Tankyrase research could help complement experimental methods and facilitate ligand/structure-based design and discovery of small molecule inhibitors with enhanced selectivity.

Drug Discovery Researches on Modulators of Lysine-Modifying Enzymes Based on Strategic Chemistry Approaches.

Enzymatic and post-translational modifications (PTMs) such as ubiquitination, acetylation, and methylation occur at lysine residues. The PTMs play critical roles in the regulation of the protein functions, and thus, various cellular processes. In addition, aberrations of the PTMs are associated with various diseases, such as cancer and neurodegenerative disorders. Therefore, we hypothesized that modulation of the PTMs and normalization of the PTM abnormalities could be useful as methods to control various cellular mechanisms and as a therapeutic strategy, respectively. To modulate the PTMs, we have focused on lysine-modifying enzymes and have pursued drug discovery researches on ubiquitination inducers, lysine deacetylase (KDAC) inhibitors, and lysine demethylase (KDM) inhibitors. For the identification of the modulators, we have used not only conventional drug design, such as structure-based drug design (SBDD) and ligand-based drug design (LBDD), but also "strategic chemistry approaches," such as drug design based on enzyme catalytic mechanism. As a result, we have identified several modulators which have pharmacological effects in animal models or in cellular studies. In this review, focusing on the drug design based on enzyme catalytic mechanism, our drug discovery researches have been discussed.

Prospects for Discovering the Secondary Metabolites of Cordyceps Sensu Lato by the Integrated Strategy.

Some species of Cordyceps sensu lato are famous Chinese herbs with significant biological activities, often used as edible food and traditional medicine in China. Cordyceps represents the largest entomopathogenic group of fungi, including 40 genera and 1339 species in three families and incertae sedis of Hypocreales. Most of the Cordyceps-derivatives have been approved clinically for the treatment of various diseases such as diabetes, cancers, inflammation, cardiovascular, renal and neurological disorders and are used worldwide as supplements and herbal drugs, but there is still need for highly efficient Cordyceps-derived drugs for fatal diseases with approval of the U.S. Food and Drug Administration. Computer-aided drug design concepts could improve the discovery of putative Cordyceps- derived medicine within less time and low budget. The integration of computer-aided drug design methods with experimental validation has contributed to the successful discovery of novel drugs. This review focused on modern taxonomy, active metabolites, and modern drug design techniques that could accelerate conventional drug design and discovery of Cordyceps s. l. Successful application of computer-aided drug design methods in Cordyceps research has been discussed. It has been concluded that computer-aided drug design techniques could influence the multiple target-focused drug design, because each metabolite of Cordyceps has shown significant activities for the various diseases with very few or no side effects.

The Power of Spheres

DNA or RNA molecules, arranged into spherical shapes, can attack brain cancers and other illnesses that evade conventional drug design. DNA or RNA molecules, arranged into spherical shapes, can attack brain cancers and other illnesses that evade conventional drug design.

Ligand-based design identifies a potent NUPR1 inhibitor exerting anticancer activity via necroptosis.

Intrinsically disordered proteins (IDPs) are emerging as attractive drug targets by virtue of their prevalence in various diseases including cancer. Drug development targeting IDPs is challenging because they have dynamical structure features and conventional drug design is not applicable. NUPR1 is an IDP playing an important role in pancreatic cancer. We previously reported that Trifluoperazine (TFP), an antipsychotic agent, was capable of binding to NUPR1 and inhibiting tumors growth. Unfortunately, TFP showed strong central nervous system side-effects. In this work, we undertook a multidisciplinary approach to optimize TFP, based on the synergy of computer modeling, chemical synthesis, and a variety of biophysical, biochemical and biological evaluations. A family of TFP-derived compounds was produced and the most active one, named ZZW-115, showed a dose-dependent tumor regression with no neurological effects and induced cell death mainly by necroptosis. This study opens a new perspective for drug development against IDPs, demonstrating the possibility of successful ligand-based drug design for such challenging targets.

The Open Form Inducer Approach for Structure-Based Drug Design.

Many open form (OF) structures of drug targets were obtained a posteriori by analysis of co-crystals with inhibitors. Therefore, obtaining the OF structure of a drug target a priori will accelerate development of potent inhibitors. In addition to its small active site, Trypanosoma cruzi dihydroorotate dehydrogenase (TcDHODH) is fully functional in its monomeric form, making drug design approaches targeting the active site and protein-protein interactions unrealistic. Therefore, a novel a priori approach was developed to determination the TcDHODH active site in OF. This approach consists of generating an "OF inducer" (predicted in silico) to bind the target and cause steric repulsion with flexible regions proximal to the active site that force it open. We provide the first proof-of-concept of this approach by predicting and crystallizing TcDHODH in complex with an OF inducer, thereby obtaining the OF a priori with its subsequent use in designing potent and selective inhibitors. Fourteen co-crystal structures of TcDHODH with the designed inhibitors are presented herein. This approach has potential to encourage drug design against diseases where the molecular targets are such difficult proteins possessing small AS volume. This approach can be extended to study open/close conformation of proteins in general, the identification of allosteric pockets and inhibitors for other drug targets where conventional drug design approaches are not applicable, as well as the effective exploitation of the increasing number of protein structures deposited in Protein Data Bank.

Unique Functional Materials Derived from β-Amino Acid Oligomers

The unique structures formed by β-amino acid oligomers, or β-peptide foldamers, have been studied for almost two decades, which has led to the discovery of several distinctive structures and bioactive molecules. Recently, this area of research has expanded from conventional peptide drug design to the formation of assemblies and nanomaterials by peptide self-assembly. The unique structures formed by β-peptides give rise to a set of new materials with altered properties that differ from conventional peptide-based materials; such new materials may be useful in several bio- and nanomaterial applications.

Dynamic Modelling Reveals 'Hotspots' on the Pathway to Enzyme-Substrate Complex Formation.

Dihydrodipicolinate synthase (DHDPS) catalyzes the first committed step in the diaminopimelate pathway of bacteria, yielding amino acids required for cell wall and protein biosyntheses. The essentiality of the enzyme to bacteria, coupled with its absence in humans, validates DHDPS as an antibacterial drug target. Conventional drug design efforts have thus far been unsuccessful in identifying potent DHDPS inhibitors. Here, we make use of contemporary molecular dynamics simulation and Markov state models to explore the interactions between DHDPS from the human pathogen Staphylococcus aureus and its cognate substrate, pyruvate. Our simulations recover the crystallographic DHDPS-pyruvate complex without a priori knowledge of the final bound structure. The highly conserved residue Arg140 was found to have a pivotal role in coordinating the entry of pyruvate into the active site from bulk solvent, consistent with previous kinetic reports, indicating an indirect role for the residue in DHDPS catalysis. A metastable binding intermediate characterized by multiple points of intermolecular interaction between pyruvate and key DHDPS residue Arg140 was found to be a highly conserved feature of the binding trajectory when comparing alternative binding pathways. By means of umbrella sampling we show that these binding intermediates are thermodynamically metastable, consistent with both the available experimental data and the substrate binding model presented in this study. Our results provide insight into an important enzyme-substrate interaction in atomistic detail that offers the potential to be exploited for the discovery of more effective DHDPS inhibitors and, in a broader sense, dynamic protein-drug interactions.

Big data in drug design

Big data collection in the pharmaceutical research industry has four sources, high-throughput scientific experiments, high-performance computations, automated information acquisition and office automation, and scientific publications and patents. Big data is the product and the promoter of high-performance scientific experiments, therefore the technology for mining big data is the key to future drug discovery. However, big data brings greater challenges, such as, storage, retrieval, curation and quality assurance, sharing/transfer, analysis, visualization, modeling and computing complexities. This review outlines the current progress of processing big data in the drug design field. These problems may be resolved by adopting cloud computing and high-performance computing technologies, and parallelizing existing chemoinformatics and bioinformatics programs. Machine-learning approaches involving Bayesian learning methods and other methods, such as support vector machine and recursive petitioning, can be used for big data mining. Recent progress includes parallelized and GPU-accelerated molecular dynamics simulation technology, enhanced molecular docking technology, new parallelized algorithms for shape-based virtual screening, free-energy landscape calculations, and machine-learning algorithms for big chemical structural data mining. Big data from drug discoveries will increase, so conventional drug design software and methods need to be upgraded. This is a long-term project and we highlight the tasks that need to be accomplished to meet this goal.

Sub-lethal Activity of Small Molecules from Natural Sources and their Synthetic Derivatives Against Biofilm Forming Nosocomial Pathogens

Nowadays, the patient safety is seriously jeopardized by the emergence and spread of nosocomial pathogens in the form of biofilm that is resistant to traditional and affordable antimicrobials. Although advances in organic synthesis have extended the lifetime of classic antibiotics through synthetic modifications, the search of innovative antibiofilm compounds from natural sources can provide new templates, novel targets and unique mechanisms that should have advantages over known antimicrobial agents. Testing sub-lethal concentrations of crude extracts and/or isolated compounds from plants and microorganisms is critical to acting on mechanisms subtler than the killing activity, e.g. those influencing the multicellular behavior, offering an elegant way to develop novel antimicrobial-free antibiofilm strategies. Herein we discussed the search and biological activity of small molecules from natural sources and their synthetic derivatives able to modulate biofilm genesis of nosocomial pathogens through non-microbicidal mechanisms (sub-lethal concentrations). The present work offers an overview about the approaches applied to the discovery of lead small molecules including a) conventional drug design methods like screening of chemical compounds obtained from nature and b) computer- aided drug design approaches. Finally, a classification (not exhaustive but representative) based on the natural origin of small molecules and their synthetic derivatives was reported. The information presented in this review should be of interest to a broad range of disciplines and represents an effort to summarize experimental research and advances in this field.

Modeling of Molecular Interaction between Apoptin, BCR-Abl and CrkL - An Alternative Approach to Conventional Rational Drug Design

In this study we have calculated a 3D structure of apoptin and through modeling and docking approaches, we show its interaction with Bcr-Abl oncoprotein and its downstream signaling components, following which we confirm some of the newly-found interactions by biochemical methods. Bcr-Abl oncoprotein is aberrantly expressed in chronic myelogenous leukaemia (CML). It has several distinct functional domains in addition to the Abl kinase domain. The SH3 and SH2 domains cooperatively play important roles in autoinhibiting its kinase activity. Adapter molecules such as Grb2 and CrkL interact with proline-rich region and activate multiple Bcr-Abl downstream signaling pathways that contribute to growth and survival. Therefore, the oncogenic effect of Bcr-Abl could be inhibited by the interaction of small molecules with these domains. Apoptin is a viral protein with well-documented cancer-selective cytotoxicity. Apoptin attributes such as SH2-like sequence similarity with CrkL SH2 domain, unique SH3 domain binding sequence, presence of proline-rich segments, and its nuclear affinity render the molecule capable of interaction with Bcr-Abl. Despite almost two decades of research, the mode of apoptin's action remains elusive because 3D structure of apoptin is unavailable. We performed in silico three-dimensional modeling of apoptin, molecular docking experiments between apoptin model and the known structure of Bcr-Abl, and the 3D structures of SH2 domains of CrkL and Bcr-Abl. We also biochemically validated some of the interactions that were first predicted in silico. This structure-property relationship of apoptin may help in unlocking its cancer-selective toxic properties. Moreover, such models will guide us in developing of a new class of potent apoptin-like molecules with greater selectivity and potency.

Polypharmacology Directed Compound Data Mining: Identification of Promiscuous Chemotypes with Different Activity Profiles and Comparison to Approved Drugs

Increasing evidence that many pharmaceutically relevant compounds elicit their effects through binding to multiple targets, so-called polypharmacology, is beginning to change conventional drug discovery and design strategies. In light of this paradigm shift, we have mined publicly available compound and bioactivity data for promiscuous chemotypes. For this purpose, a hierarchy of active compounds, atomic property based scaffolds, and unique molecular topologies were generated, and activity annotations were analyzed using this framework. Starting from ∼35 000 compounds active against human targets with at least 1 μM potency, 33 chemotypes with distinct topology were identified that represented molecules active against at least 3 different target families. Network representations were utilized to study scaffold-target family relationships and activity profiles of scaffolds corresponding to promiscuous chemotypes. A subset of promiscuous chemotypes displayed a significant enrichment in drugs over bioactive compounds. A total of 190 drugs were identified that had on average only 2 known target annotations but belonged to the 7 most promiscuous chemotypes that were active against 8-15 target families. These drugs should be attractive candidates for polypharmacological profiling.

A Multidimensional Strategy to Detect Polypharmacological Targets in the Absence of Structural and Sequence Homology

Conventional drug design embraces the “one gene, one drug, one disease” philosophy. Polypharmacology, which focuses on multi-target drugs, has emerged as a new paradigm in drug discovery. The rational design of drugs that act via polypharmacological mechanisms can produce compounds that exhibit increased therapeutic potency and against which resistance is less likely to develop. Additionally, identifying multiple protein targets is also critical for side-effect prediction. One third of potential therapeutic compounds fail in clinical trials or are later removed from the market due to unacceptable side effects often caused by off-target binding. In the current work, we introduce a multidimensional strategy for the identification of secondary targets of known small-molecule inhibitors in the absence of global structural and sequence homology with the primary target protein. To demonstrate the utility of the strategy, we identify several targets of 4,5-dihydroxy-3-(1-naphthyldiazenyl)-2,7-naphthalenedisulfonic acid, a known micromolar inhibitor of Trypanosoma brucei RNA editing ligase 1. As it is capable of identifying potential secondary targets, the strategy described here may play a useful role in future efforts to reduce drug side effects and/or to increase polypharmacology.

Adsorption of xenon on a protein arising from the translational motion of solvent molecules

A simple method was used to predict binding sites and to calculate the binding free energy for a xenon atom on a protein to determine the importance of the translational motion of water molecules in molecular recognition. We examined xenon bound on myoglobin and on a fragment of ammonium transporter. Despite the simplicity of our method, the predicted binding sites and the experimental results agree very well, and the estimated values of the free energy gain are also reasonable. We discuss the van der Waals picture of molecular recognition between a protein and a small hydrophobic molecule, such as an anesthetic molecule, which gives us a simple physical justification for the idea of a "lock and key" relationship used in conventional structure-based drug design programs.

Computational approaches for drug design and discovery: An overview

The process of drug discovery is very complex and requires an interdisciplinary effort to design effective and commercially feasible drugs. The objective of drug design is to find a chemical compound that can fit to a specific cavity on a protein target both geometrically and chemically. After passing the animal tests and human clinical trials, this compound becomes a drug available to patients. The conventional drug design methods include random screening of chemicals found in nature or synthesized in laboratories. The problems with this method are long design cycle and high cost. Modern approach including structure-based drug design with the help of informatic technologies and computational methods has speeded up the drug discovery process in an efficient manner. Remarkable progress has been made during the past five years in almost all the areas concerned with drug design and discovery. An improved generation of softwares with easy operation and superior computational tools to generate chemically stable and worthy compounds with refinement capability has been developed. These tools can tap into cheminformation to shorten the cycle of drug discovery, and thus make drug discovery more cost-effective. A complete overview of drug discovery process with comparison of conventional approaches of drug discovery is discussed here. Special emphasis is given on computational approaches for drug discovery along with salient features and applications of the softwares used in de novo drug designing. How to Cite this Article Pubmed Style Baldi A. Approaches for Drug Design and Discovery: An SRP. 2010; 1(1): 99-105. doi:10.4103/0975-8453.59519 Web Style Baldi A. Approaches for Drug Design and Discovery: An http://www.sysrevpharm.org/?mno=302644637 [Access: March 28, 2021]. doi:10.4103/0975-8453.59519 AMA (American Medical Association) Style Baldi A. Approaches for Drug Design and Discovery: An SRP. 2010; 1(1): 99-105. doi:10.4103/0975-8453.59519 Vancouver/ICMJE Style Baldi A. Approaches for Drug Design and Discovery: An SRP. (2010), [cited March 28, 2021]; 1(1): 99-105. doi:10.4103/0975-8453.59519 Harvard Style Baldi A (2010) Approaches for Drug Design and Discovery: An SRP, 1 (1), 99-105. doi:10.4103/0975-8453.59519 Turabian Style Baldi A. 2010. Approaches for Drug Design and Discovery: An Systematic Reviews in Pharmacy, 1 (1), 99-105. doi:10.4103/0975-8453.59519 Chicago Style Baldi A. Computational Approaches for Drug Design and Discovery: An Overview. Systematic Reviews in Pharmacy 1 (2010), 99-105. doi:10.4103/0975-8453.59519 MLA (The Modern Language Association) Style Baldi A. Computational Approaches for Drug Design and Discovery: An Overview. Systematic Reviews in Pharmacy 1.1 (2010), 99-105. Print. doi:10.4103/0975-8453.59519 APA (American Psychological Association) Style Baldi A (2010) Approaches for Drug Design and Discovery: An Systematic Reviews in Pharmacy, 1 (1), 99-105. doi:10.4103/0975-8453.59519

Protein–protein interactions as targets for small-molecule therapeutics in cancer

Small-molecule inhibition of the direct protein-protein interactions that mediate many important biological processes is an emerging and challenging area in drug design. Conventional drug design has mainly focused on the inhibition of a single protein, usually an enzyme or receptor, since these proteins often contain a clearly defined ligand-binding site with which a small-molecule drug can be designed to interact. Designing a small molecule to bind to a protein-protein interface and subsequently inhibit the interaction poses several challenges, including the initial identification of suitable protein-protein interactions, the surface area of the interface (it is often large), and the location of 'hot spots' (small regions suitable for drug binding). This article reviews the general approach to designing inhibitors of protein-protein interactions, and then focuses on recent advances in the use of small molecules targeted against a variety of protein-protein interactions that have therapeutic potential for cancer.