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Reliable Biomedical Applications Using AI Models
Published in Punit Gupta, Dinesh Kumar Saini, Rohit Verma, Healthcare Solutions Using Machine Learning and Informatics, 2023
Shambhavi Mishra, Tanveer Ahmed, Vipul Mishra
Artificial intelligence is becoming increasingly effective in drug research. Hundreds of new opportunities and targets have evolved due to proteomic, genomic, and structural studies. Dealing with microscopic images can be difficult because the results of any given experiment can vary greatly from batch to batch. Temperature fluctuations and exposure period variations can all provide false results that are unrelated to the study or the action of a prospective therapeutic molecule. In addition, a large number of factors can be used in the research. In data-driven drug discovery, keeping track of and differentiating the effects is a major challenge. Fortunately, AI can assist in overcoming these obstacles, particularly during the virtual high throughput screening phase, resulting in a more efficient, less expensive, and speedier drug discovery process. In [54], the authors show how AI is rapidly changing the drug discovery field. Structure-based drug design (SBDD) is also becoming popular. The authors of [55] focus on methods and algorithms for SBDD, including de novo and virtual screening drug design. They also emphasize AI methods used for drug discovery and discuss the challenges of handling the large data created by combinatorial chemistry. The authors state that AI and deep learning are important components of statistical machine learning approaches for integrating and analyzing enormous data sets. They also point out that although SBDD has seen a visible improvement in drug discovery, more consistent solutions need to be developed.
Swarm Intelligence and Evolutionary Algorithms for Drug Design and Development
Published in Sandeep Kumar, Anand Nayyar, Anand Paul, Swarm Intelligence and Evolutionary Algorithms in Healthcare and Drug Development, 2019
Drug design, also termed as the general drug design, is a research methodology of finding new medications based upon the knowledge of the biology targets [1]. The drugs or medications are nothing but the organic small molecules that activate or inhibit the function of a biomolecule-like protein which results in a therapeutic benefit to the patient [2,3]. The procedure of drug design is about designing the molecules which are complementary in both the shape and charge to the bimolecular target which they would interact as well as bind with. The drug design process majorly, but not necessarily, depends upon the computer modelling techniques [4]. This style of modelling is otherwise known as computer-aided drug design. On the other hand, the drug designing process lying over the knowledge of three-dimensional structure of the biomolecular target is known as the “structure-based drug design” [5].
Computer-Aided Drug Design for the Identification of Multi-Target Directed Ligands (MTDLs) in Complex Diseases: An Overview
Published in Peter Grunwald, Pharmaceutical Biocatalysis, 2019
Computational or in silico studies play an important role in the drug discovery and drug design paradigm by providing guidance towards the lead identification, lead optimization, prioritization of chemicals before initiating the synthetic or experimental analysis and designing superior analogues. Notably, in silico techniques complement the 3Rs approach, i.e., replacement, refinement, and reduction of animals in research as well as these are cost-effective and time saving. There are several available in silico techniques that can be divided into two major application areas, i.e., structure-based drug design and ligand-based drug design (Huang et al., 2010). Structure-based drug design relies on three-dimensional (3D) structural knowledge of the target protein (enzyme or receptor) and its binding sites to investigate vital inter-molecular interactions as well as their corresponding binding energy. On the other hand, ligand-based drug design relies on knowledge of ligands with known binding information with the target of interest. Notably, both type of techniques together become a powerful in silico tool to design potential ligands against one or multiple targets.
What are the current trends in G protein-coupled receptor targeted drug discovery?
Published in Expert Opinion on Drug Discovery, 2023
Vicent Casadó, Verònica Casadó-Anguera
The method of structure-based drug design can be used to perform virtual screenings of large libraries of compounds that have not been synthesized, saving experimental analysis and synthesis costs. Nevertheless, in silico methods sacrifice accuracy in affinity determination of GPCR ligands for speed. As a consequence, a large amount of false-positives are usually detected. In fact, in the best scenario, only 10% from the promising ligands detected with in silico methods are finally experimentally confirmed [11]. For all these reasons, the in silico structure-based method should be considered as a complementary approach to classical experimental screening or to high-throughput screening. Actually, until 2019, only one FDA-approved allosteric drug has been discovered exclusively using in silico methods (enasidenib, an inhibitor of a digestive enzyme approved in 2017 for acute myeloid leukemia) [14]. In order to perform experimental screenings to determine the affinity of a compound for its receptor, several techniques can be used such as binding assays (e.g. saturation, competition and dissociation radioligand binding assays) or biophysical screening methods such as homogeneous time resolved fluorescence (HTRF), NanoBRET and surface plasmon resonance [13,15]. Experimental screenings typically start with a single drug concentration and, with the best hits, full dose-response curves are performed. Affinity assays must be accompanied with functional assays to determine the signaling pathways affected (see next section).
Radio-protective efficacy of Gymnema sylvestre on Pangasius sutchi against gamma (60Co) irradiation
Published in International Journal of Radiation Biology, 2022
Pamela Sinha, Kantha Devi Arunachalam, Santhosh Kumar Nagarajan, Thirumurthy Madhavan, Arumugam R. Jayakumar, Mohamed Saiyad Musthafa
Molecular docking is one of the most frequently used methods in structure-based drug design, due to its ability to predict the binding-conformation of small molecule ligands to the appropriate target binding site. Characterization of the binding behavior plays an important role in rational design of drugs, as well as to elucidate fundamental biochemical processes; and predicting and prioritizing large library of molecules for a particular action is highly desirable (Chong et al. 2006; Kim et al. 2009). Elucidation of structural similarity of drugs and their known side-effects are useful in the establishment and analysis of networks responsible for poly-pharmacology (Johnson and Maggiora 1990; Campillos et al. 2008). The importance of ‘Amifostine’ and G. sylvestre components and its possible implications in radiation protection has made us to carry out a systematic effort to find whether the known radio-protectants carry this function by following in silico approaches.
Applications of fluorine to the construction of bioisosteric elements for the purposes of novel drug discovery
Published in Expert Opinion on Drug Discovery, 2021
Human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) is a validated target for AIDS therapy with both nucleoside/nucleotide-based (NRTISs/NtRTIs) and non-nucleoside (NNRTIs) RT inhibitors approved for clinical use [111–113]. In addition, the target presents well-defined biochemical mechanisms as well as a wealth of structural information thus facilitating rational structure-based drug design efforts. Owing to their potent antiviral activity and high selectivity, NNRTIs are widely used in combination antiretroviral therapy. First-generation NNRTIs have been shown to lose activity against single-point mutations, while adverse effects have been observed with the second-generation inhibitors as well as the emergence of resistant dual-mutations [114,115]. Given this, there is a need to develop next generation NNRTIs with greater potency, improved drug-resistance profiles, and less toxicity [116].