Fragment based drug design

Rational approaches towards drug development have emerged as one of the most promising ways among the tedious conventional procedures with the aim of redefining the drug discovery process. The need of current medical system is demanding a much precise, faster and reliable approaches in parallel to faster growing technology for development of drugs with more intrinsic action and fewer side effects. Systematic and well-defined diagnostic studies have revealed the specific causes of disease and related targets for drug development. Designing a drug as per the specific target, studying it in-silico prior to its development has been proved as an added benefit to the studies. Many approaches like structure based drug design, fragment based drug design and ligand based drug design are been in practice for the drug discovery and development with the similar fundamental theory. Fragment based drug design utilizes a library of fragments designed specifically for the concerned target and these fragments are studied further before screening with virtual methods as well as with biophysical methods. The process follows a well-defined pathway which moulds a fragment into a perfect drug candidate. In this chapter we have tried to cover all the basic aspects of fragment based drug design and related technologies.

Comprehensive analysis of commercial fragment libraries

Screening of fragment libraries is a valuable approach to the drug discovery process. The quality of the library is one of the keys to success, and more particularly the design...

The iterative application of a large chemical space in the drug discovery process

Aim. To demonstrate the advantages of large-scale virtual libraries generated using chemical protocols previously validated in primary steps of the drug discovery process.Results and discussion. Two validated parallel chemistry protocols reported earlier were used to create the chemical space. It was then sampled based on diversity metric, and the sample was subjected to the virtual screening on BRD4 target. Hits of virtual screening were synthesized and tested in the thermal shift assay.Experimental part. The chemical space was generated using commercially available building blocks and synthetic protocols suitable for parallel chemistry and previously reported. After narrowing it down, using MedChem filters, the resulting sub-space was clustered based on diversity metrics. Centroids of the clusters were put to the virtual screening against the BRD4 active center. 29 Hits from the docking were synthesized and subjected to the thermal shift assay with BRD4, and 2 compounds showed noticeable dTm.Conclusions. A combination of cheminformatics and molecular docking was applied to find novel potential binders for BRD4 from a large chemical space. The selected set of predicted molecules was synthesized with a 72 % success rate and tested in a thermal shift assay to reveal a 6 % hit rate. The selection can be performed iteratively to fast support of the drug discovery.

Screening of Potential Indonesia Herbal Compounds Based on Multi-Label Classification for 2019 Coronavirus Disease

Coronavirus disease 2019 pandemic spreads rapidly and requires an acceleration in the process of drug discovery. Drug repurposing can help accelerate the drug discovery process by identifying new efficacy for approved drugs, and it is considered an efficient and economical approach. Research in drug repurposing can be done by observing the interactions of drug compounds with protein related to a disease (DTI), then predicting the new drug-target interactions. This study conducted multilabel DTI prediction using the stack autoencoder-deep neural network (SAE-DNN) algorithm. Compound features were extracted using PubChem fingerprint, daylight fingerprint, MACCS fingerprint, and circular fingerprint. The results showed that the SAE-DNN model was able to predict DTI in COVID-19 cases with good performance. The SAE-DNN model with a circular fingerprint dataset produced the best average metrics with an accuracy of 0.831, recall of 0.918, precision of 0.888, and F-measure of 0.89. Herbal compounds prediction results using the SAE-DNN model with the circular, daylight, and PubChem fingerprint dataset resulted in 92, 65, and 79 herbal compounds contained in herbal plants in Indonesia respectively.

Docking Molecular Simulation Of Secondary Metabolic Compounds Annona Muricata As Anti-Cancer

This study aims to test and determine the affinity and molecular mechanism of Annona muricata to COX-2 target protein, which can be used to test the potential of Annona muricata as an anticancer drug using the molecular docking in silico method (computer modeling). By identifying and optimizing guide molecules in the drug discovery process, this computational chemical technique can be utilized to accelerate the selection of compounds to be isolated and synthesized. The research use descriptive quantitative as a research design and the experimental factorial design as an approach. The results of this study indicate that curcumin and its analogues have potential to became anticancer, and can be used for further drug development related to anticancer.

Challenges, Advances and Opportunities in Exploring Natural Products to Control Arboviral Disease Vectors

Natural products constitute an important source of molecules for product development. However, despite numerous reports of compounds and active extracts from biodiversity, poor and developing countries continue to suffer with endemic diseases caused by arboviral vectors, including dengue, Zika, chikungunya and urban yellow fever. Vector control remains the most efficient disease prevention strategy. Wide and prolonged use of insecticides has resulted in vector resistance, making the search for new chemical prototypes imperative. Considering the potential of natural products chemistry for developing natural products-based products, including insecticides, this contribution discusses the general aspects and specific characteristics involved in the development of drug leads for vector control. Throughout this work, we highlight the obstacles that need to be overcome in order for natural products compounds to be considered promising prototypes. Moreover, we analyze the bottlenecks that should be addressed, together with potential strategies, to rationalize and improve the efficiency of the drug discovery process.

Evaluation of the Effectiveness of Herbal Components Based on Their Regulatory Signature on Carcinogenic Cancer Cells

Predicting cancer cells’ response to a plant-derived agent is critical for the drug discovery process. Recently transcriptomes advancements have provided an opportunity to identify regulatory signatures to predict drug activity. Here in this study, a combination of meta-analysis and machine learning models have been used to determine regulatory signatures focusing on differentially expressed transcription factors (TFs) of herbal components on cancer cells. In order to increase the size of the dataset, six datasets were combined in a meta-analysis from studies that had evaluated the gene expression in cancer cell lines before and after herbal extract treatments. Then, categorical feature analysis based on the machine learning methods was applied to examine transcription factors in order to find the best signature/pattern capable of discriminating between control and treated groups. It was found that this integrative approach could recognize the combination of TFs as predictive biomarkers. It was observed that the random forest (RF) model produced the best combination rules, including AIP/TFE3/VGLL4/ID1 and AIP/ZNF7/DXO with the highest modulating capacity. As the RF algorithm combines the output of many trees to set up an ultimate model, its predictive rules are more accurate and reproducible than other trees. The discovered regulatory signature suggests an effective procedure to figure out the efficacy of investigational herbal compounds on particular cells in the drug discovery process.

Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2

Deep learning significantly accelerates the drug discovery process, and contributes to global efforts to stop the spread of infectious diseases. Besides enhancing the efficiency of screening of antimicrobial compounds against a broad spectrum of pathogens, deep learning has also the potential to efficiently and reliably identify drug candidates against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Consequently, deep learning has been successfully used for the identification of a number of potential drugs against SARS-CoV-2, including Atazanavir, Remdesivir, Kaletra, Enalaprilat, Venetoclax, Posaconazole, Daclatasvir, Ombitasvir, Toremifene, Niclosamide, Dexamethasone, Indomethacin, Pralatrexate, Azithromycin, Palmatine, and Sauchinone. This mini-review discusses recent advances and future perspectives of deep learning-based SARS-CoV-2 drug discovery.

Targeting small GTPases and their downstream pathways with intracellular macromolecule binders to define alternative therapeutic strategies in cancer

The RAS superfamily of small GTPases regulates major physiological cellular processes. Mutation or deregulation of these small GTPases, their regulators and/or their effectors are associated with many diseases including cancer. Hence, targeting these classes of proteins is an important therapeutic strategy in cancer. This has been recently achieved with the approval of the first KRASG12C covalent inhibitors for the clinic. However, many other mutants and small GTPases are still considered as ‘undruggable' with small molecule inhibitors because of a lack of well-defined pocket(s) at their surface. Therefore, alternative therapeutic strategies have been developed to target these proteins. In this review, we discuss the use of intracellular antibodies and derivatives — reagents that bind their antigen inside the cells — for the discovery of novel inhibitory mechanisms, targetable features and therapeutic strategies to inhibit small GTPases and their downstream pathways. These reagents are also versatile tools used to better understand the biological mechanisms regulated by small GTPases and to accelerate the drug discovery process.

Organ-on-chip applications in drug discovery: an end user perspective

Organ-on-chip (OoC) systems are in vitro microfluidic models that mimic the microstructures, functions and physiochemical environments of whole living organs more accurately than two-dimensional models. While still in their infancy, OoCs are expected to bring ground-breaking benefits to a myriad of applications, enabling more human-relevant candidate drug efficacy and toxicity studies, and providing greater insights into mechanisms of human disease. Here, we explore a selection of applications of OoC systems. The future directions and scope of implementing OoCs across the drug discovery process are also discussed.