Drug Discovery

Objective: The objective of this paper is to highlight the state-of-the-art machine learning (ML) techniques in computational docking. The use of smart computational methods in the life cycle of drug design is relatively a recent development that has gained much popularity and interest over the last few years. Central to this methodology is the notion of computational docking which is the process of predicting the best pose (orientation + conformation) of a small molecule (drug candidate) when bound to a target larger receptor molecule (protein) in order to form a stable complex molecule. In computational docking, a large number of binding poses are evaluated and ranked using a scoring function. The scoring function is a mathematical predictive model that produces a score that represents the binding free energy, and hence the stability, of the resulting complex molecule. Generally, such a function should produce a set of plausible ligands ranked according to their binding stability along with their binding poses. In more practical terms, an effective scoring function should produce promising drug candidates which can then be synthesized and physically screened using high throughput screening process. Therefore, the key to computer-aided drug design is the design of an efficient highly accurate scoring function (using ML techniques). Methods: The methods presented in this paper is specifically based on ML techniques. Despite many traditional techniques have been proposed, the performance was generally poor. Only in the last few years started the application of the ML technology in the design of scoring functions; and the results have been very promising.
Material: The ML-based techniques are based on various molecular features extracted from the abundance of protein-ligand information in the public molecular databases, e.g., protein data bank bind (PDBbind).
Results: In this paper, we present this paradigm shift elaborating on the main constituent elements of the ML approach to molecular docking along with the state-of-the-art research in this area. For instance, the best random forest (RF)-based scoring function (Li, 2014) on PDBbind v2007 achieves a Pear- son correlation coefficient between the predicted and experimentally deter- mined binding affinities of 0.803 while the best conventional scoring function achieves 0.644 (Cheng, 2009). The best RF-based ranking power (Ashtawy, 2012) ranks the ligands correctly based on their experimentally determined binding affinities with accuracy 62.5% and identifies the top binding ligand with accuracy 78.1%.
Conclusions: We conclude with open questions and potential future research directions that can be pursued in smart computational docking; using molecular features of different nature (geometrical, energy terms, pharmacophore), advanced ML techniques (e.g., deep learning), combining more than one ML models.
Keywords:
machine learning, random forest, support vector machine, drug discovery, computational docking, scoring function, virtual screening, complex binding affinity, ligands ranking accuracy, force field interaction, pharmacophore fingerprint.

Novel microfluidic tools allow new ways to manufacture and test drug delivery systems. Organ-on-a-chip systems - microscale recapitulations of complex organ functions - promise to improve the drug development pipeline. This review highlights the importance of integrating microfluidic networks with 3D tissue engineered models to create organ-on-a-chip platforms, able to meet the demand of creating robust preclinical screening models. Specific examples are cited to demonstrate the use of these systems for studying the performance of drug delivery vectors and thereby reduce the discrepancies between their performance at preclinical and clinical trials. We also highlight the future directions that need to be pursued by the research community for these proof-of-concept studies to achieve the goal of accelerating clinical translation of drug delivery nanoparticles.

Novel microfluidic tools allow new ways to manufacture and test drug delivery systems. Organ-on-a-chip systems - microscale recapitulations of complex organ functions - promise to improve the drug development pipeline. This review highlights the importance of integrating microfluidic networks with 3D tissue engineered models to create organ-on-a-chip platforms, able to meet the demand of creating robust preclinical screening models. Specific examples are cited to demonstrate the use of these systems for studying the performance of drug delivery vectors and thereby reduce the discrepancies between their performance at preclinical and clinical trials. We also highlight the future directions that need to be pursued by the research community for these proof-of-concept studies to achieve the goal of accelerating clinical translation of drug delivery nanoparticles.

Cleanroom contamination can arise from a number of sources. Most contamination within the pharmaceutical facility can be traced to humans working in cleanrooms. The paper discusses staff gowning and personnel behavior in pharmaceutical cleanrooms, and how cleanroom risk can be minimized. The human skin ecosystem is discussed. The Human Microbiome Project (HMP) from the US NIH characterized microorganisms found in association with both healthy and diseased humans. Information from this project has great impact on cleanroom activities including gowning practices. Topics associated with cleanroom garments are discussed including fabric types,

Unlike traditional biological research that focuses on a small set of components, systems biology studies the complex interactions among a large number of genes, proteins, and other elements of biological networks and systems. Host-pathogen systems biology examines the interactions between the components of two distinct organisms: a microbial or viral pathogen and its animal host. With the availability of complete genomic sequences of a variety of hosts and pathogens, together with breakthroughs in proteomics, metabolomics, and other experimental areas, the investigation of host-pathogen systems on a multitude of levels of detail has come within reach. This chapter introduces methods and approaches for the analysis of host-pathogen systems. We will particularly emphasize the role of biochemical networks for the study of complex relationships across species boundaries. Although the research area of host-pathogen systems biology spans multiple spatial and temporal scales, we will focus on the molecular and cellular aspects of pathogen-host interactions. We will cover the construction of biochemical networks, the identification of functional response sub-networks, and their comparative network analysis. These methods find application in the identification of host markers and drug targets for further drug development and therapeutic interventions. We will also provide a brief review of other modeling techniques and applications within host-pathogen systems biology, including rule-based modeling of signal transduction pathways, immune system models, and physiological top-down approaches.

The pharmaceutical industry is facing enormous challenges due to high drug attribution rates. For the past decades, novel methods have been developed for safety and efficacy testing, as well as for improving early development stages. screening methods for drug-receptor binding are considered to be good alternatives for decreasing costs in the identification of drug candidates. However, these methods require lengthy and troublesome labeling steps. Biosensors hold great promise due to the fact that label-free detection schemes can be designed in an easy and low-cost manner. In this paper, for the first time in the literature, we aimed to compare the potential of label-free optical and impedimetric electrochemical biosensors for the screening of antipsychotic drugs (APDs) based on their binding properties to dopamine receptors. Particularly, we have chosen a currently-used atypical antipsychotic drug (Buspirone) for investigating its dopamine D3 receptor (D3R) binding properties using ...

This article focuses on issues concerning science and technology relationships posed by the emergence of a new drug discovery method, namely, combinatorial chemistry and biology. We assess the scientific content of combinatorial chemistry and biology using citations in patents to scientific journals and compare this research platform with biotechnology. We also identify the institutional affiliation of all the authors of the cited papers, which leads us to an analysis of knowledge spillovers between the main participants in the research network. Finally, we examine the relevance of localisation in the process of knowledge exchange with regard to EU countries and the US. The result of the analysis provides evidence to support the view that the inventive capacity of a country is dependent upon the basic research which is carried out, especially in universities and public research centres located in the inventor's country.

Humans around the globe probably discovered natural remedies against disease and cancer by trial and error over the millennia. Biomolecular archaeological analyses of ancient organics, especially plants dissolved or decocted as fermented beverages, have begun to reveal the preliterate histories of traditional pharmacopeias, which often date back thousands of years earlier than ancient textual, ethnohistorical, and ethnological evidence. In this new approach to drug discovery, two case studies from ancient Egypt and China illustrate how ancient medicines can be reconstructed from chemical and archaeological data and their active compounds delimited for testing their anticancer and other medicinal effects. Specifically, isoscopoletin from Artemisia argyi, artemisinin from Artemisia annua, and the latter's more easily assimilated semi-synthetic derivative, artesunate, showed the greatest activity in vitro against lung and colon cancers. In vivo tests of these compounds previously u...

Standard chemistry prescribes the conversion of one or two compounds into their products. In contrast, Eintopf (one-pot) multicomponent reactions (MCRs) involve at least three different compounds. One-pot MCRs are a useful tool in combinatorial chemistry: From a mixture of educts a large number of products can be simultaneously formed in liquid phase, called a soluble molecular library. The member compounds

The extracellular signal-regulated kinases ERK1/2 represent an essential node within the RAS/RAF/MEK/ERK signaling cascade that is commonly activated by oncogenic mutations in BRAF or RAS or by upstream oncogenic signaling. While targeting upstream nodes with RAF and MEK inhibitors has proven effective clinically, resistance frequently develops through reactivation of the pathway. Simultaneous targeting of multiple nodes in the pathway, such as MEK and ERK, offers the prospect of enhanced efficacy as well as reduced potential for acquired resistance. Described herein is the discovery and characterization of GDC-0994 (22), an orally bioavailable small molecule inhibitor selective for ERK kinase activity.

Repurposing of known drugs and compounds as anticoronavirus disease 2019 (anti-COVID-19) agents through biological reevaluation of their activities, especially the anti-severe acute respiratory syndrome coronavirus 2 (anti-SARS-CoV-2) activities, is a new viable trend in drug discovery for the COVID-19 pandemic in 2020. Comprehensive inhibition of the enzymes and proteins of coronavirus and coronavirus 2 (i.e., multitarget inhibition) can be considered one of the most promising strategies for the development of highly potent remedies for COVID-19. However, almost all the reported inhibitors of the different life cycle stages of SARS-CoV-2 lack extreme potency against the major and fateful SARS-CoV-2 enzymes (e.g., RNA-dependent RNA polymerase ‘‘RdRp’’, papain-like protease ‘‘PLpro’’, and main protease ‘‘Mpro’’). Herein, two antioxidant polyphenolic 1,3,4-oxadiazole compounds previously synthesized by me were repurposed and introduced, 1,2,3-tris[5-(3,4,5-trihydroxyphenyl)-1,3,4-oxadiazol-2-yl]propan-2-ol (named CoViTris2020) and 5-[5-(7-chloro-4-hydroxyquinolin-3-yl)-1,3,4-oxadiazol-2-yl]benzene-1,2,3-triol (named ChloViD2020), as the first multitarget SARS-CoV-2 inhibitors with higher potencies than other drugs reported to date (about 65, 171, and 303.5 times for CoViTris2020 and 20, 52.5, and 93 times for ChloViD2020 compared to those of remdesivir, ivermectin, and favipiravir, respectively). These two unique 2,5-disubstituted-1,3,4-oxadiazole derivatives were computationally studied (through molecular docking in almost all SARS-CoV-2 proteins and one human protein) and biologically evaluated (through one of the most credible in vitro anti-COVID-19 assays) for their anti-COVID-19 activities. The results of the computational docking showed that CoViTris2020 and ChloViD2020 exhibited very high inhibitory binding affinities with most of the docked SARS-CoV-2/human proteins (e.g., they exhibited low binding energies of -12.00 and -9.60 kcal mol-1, respectively, with RdRp-RNA). Interestingly, the results of the biological assay showed that CoViTris2020 and ChloViD2020 exhibited very high and extremely significant anti-COVID-19 activities (anti-SARS-CoV-2 EC50 = 0.31 and 1.01 mM, respectively). Additionally, they may also be very promising lead compounds for the design and synthesis of new anti-COVID-19 agents (through structural modifications and further computational studies). Therefore, further investigations for the development of CoViTris2020 and ChloViD2020 as anti-COVID-19 drugs through in vivo biological evaluations and clinical trials are necessary. In brief, the development of CoViTris2020 and ChloViD2020 as the first two members of the new and promising class of anti-COVID-19 polyphenolic 2,5-disubstituted-1,3,4-oxadiazole derivatives will result in a therapeutic revolution for the treatment of SARS-CoV-2 infection and its accompanying COVID-19.