China
Africa
Explore chapters and articles related to this topic
Computational Drug Discovery and Development Along With Their Applications in the Treatment of Women-Associated Cancers
Published in Shazia Rashid, Ankur Saxena, Sabia Rashid, Latest Advances in Diagnosis and Treatment of Women-Associated Cancers, 2022
Rahul Kumar, Rakesh Kumar, Harsh Goel, Somorjit Singh Ningombam, Pranay Tanwar
In spite of all its advantages, there are several challenges associated with various phases of computational drug designing. The main promise is to determine the optimal target for pharmaceutical intervention. Every disease gene may not be an achievable target as well as its expression profile is confined within the transcription stage and does not indicate about its translation stage. In cases where experimental the 3D structure of target is not available or the sequence identity is <20%, then it can be determined through computational approach which is a complex process and defy its accuracy [22]. During virtual screening, compounds are used in their canonical form and neglect some key factors such as ionization, tautomerism, and protonation, which in turn reduce the significant hits [51]. The scoring algorithm used at the time of docking is intricate and fairly complex. It relies on various approximations and is sometimes out of reach [52]. Also, classical computational has the scaling limitations of simulation for larger biomolecule or complex molecular systems. After meeting with all the necessary functions to be eligible for a successful drug candidate, the majority of oncology drugs fail at the time of phase trials due to lack of safety, efficacy, strategy, and operation [53].
Phytonanotechnology
Published in Namrita Lall, Medicinal Plants for Cosmetics, Health and Diseases, 2022
Tafadzwa J. Chiome, Asha Srinivasan
Once a target is identified, it is then validated for a response, which is critical in the disease process. The next step in the drug discovery process is identification, which involves discovery of the compound that will bind and interact with the target. A library of compounds is screened against the chosen validated target, and suitable hits are grown and developed into larger lead-like compounds. Several screening techniques such as high throughput screening, fragment-based screening and/or virtual screening can be used (Baker et al., 1995). Compounds in the hit series are optimized to improve potency and selectivity, which is achieved through functional group modifications. The identified lead compounds are modified to further improve the physicochemical and biological properties, making the compounds more effective and safer while transforming them into more viable drug candidates. The optimized compounds are then taken through the drug development process, starting with preclinical trials which lead to clinical testing (Baker et al., 1995).
Plantago ovata (Isabgol) and Rauvolfia serpentina (Indian Snakeroot)
Published in Azamal Husen, Herbs, Shrubs, and Trees of Potential Medicinal Benefits, 2022
Ankur Anavkar, Nimisha Patel, Ahmad Ali, Hina Alim
In the modern era, phytomedicines have been incorporated into our medical systems. Thus, plant-derived molecules (PDMs) are being constantly used for drug discovering. The complete process of drug discovering can be rationalized by various computational approaches. Approaches such as molecular docking, ligand-based virtual screening, and molecular dynamics are currently used for reducing the cost of drug development (Pathania et al., 2015). Pathania et al., (2015) has structurally compiled 147 PDMs of R. serpentina and made a database, Serpentina DB. This database includes plant part source, chemical classification with the International Union of Pure and Applied Chemistry (IUPAC), etc. The database also identifies analogs of natural molecules on the ZINC database. Of the 147 PDMs, 122 are alkaloids, 7 iridoidglucosides, 6 phenols, 4 phytosterols and anhydronium bases each, 3 glycosides, and 1 fatty acid (Pathania et al., 2013).
Discovery of human autophagy initiation kinase ULK1 inhibitors by multi-directional in silico screening strategies
Published in Journal of Receptors and Signal Transduction, 2019
Poornimaa Mu, Ramanathan Karuppasamy
Virtual screening has become an imperative tool due to its quick and economic method in identifying novel active molecules. Prior to VS, a phase database was created using Phase module with the SDF files of DrugBank molecules. In the present study, VS was carried using a grid-based ligand docking and energetic (GLIDE) module in a hierarchical manner. Glide follows an algorithm, where it systematically searches the positions, conformations and orientation of the ligand to obtain the binding affinity of the receptor–ligand complex [33]. Initially, HTVS was performed to minimize the number of compounds in the phase database to a considerable level. Further SP docking was done which had an advanced scoring function than HTVS. Finally, the molecules were docked with XP mode that increased the efficiency of docking due to its sophisticated sampling algorithm and scoring functions [34]. Further, AutoDock Vina [35] and PatchDock [36] program were implemented for affirming the pharmacophoric interaction of the resultant hits with the target protein. AutoDock Vina adopted a novel knowledge-based scoring function together Monte Carlo sampling technique and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method for local optimization. On the other hand, PatchDock use local shape featuring algorithms in the analysis.
Discovery and antitumor evaluation of novel inhibitors of spermine oxidase
Published in Journal of Enzyme Inhibition and Medicinal Chemistry, 2019
Lidan Sun, Jianlin Yang, Yu Qin, Yanlin Wang, Hongyan Wu, You Zhou, Chunyu Cao
Pharmacophore-based virtual screening is a valuable tool in the drug discovery process and can be employed for a variety of tasks43. However, pharmacophore model has not been developed for SMO inhibitors screening during the past decades. In light of the limited structural type of SMO inhibitors, we constructed a pharmacophore model based on the interactions between Spm and SMO38–40. This pharmacophore model containing three hydrogen bond acceptors features originated from SER503, GLU192, and GLU200. Based on the above pharmacophore model and molecular docking, SI-4650 was screened out. Subsequently, biochemical experiments indicated that this virtual screening strategy is efficient for identifying SMO inhibitor. Despite more data is still needed to validate and optimise the screening pipeline, these results presented here suggested a possibly useful approach to discover new inhibitors that is complementary to the strategies of designing SMO inhibitors.
How far have decision tree models come for data mining in drug discovery?
Published in Expert Opinion on Drug Discovery, 2018
Verena Schöning, Felix Hammann
Machine learning (ML) methods assist in drug discovery mostly by way of data mining in virtual screening (VS). If the target is sufficiently characterized, say by knowledge of its three-dimensional (3D) structure or gene sequence, we can take a structure-based VS (SBVS) approach and run molecular docking and dynamics, or 3D-similarity matching experiments. More often, however, we only know a set of molecular structures and their biological activities, and so we may perform a ligand-based VS (LBVS) [1]. The results of an LBVS, which are computationally much less expensive to obtain than those of an SBVS, can be the basis of chemical database queries and, optimally, at an early stage of drug discovery enhance our understanding of how a molecule’s action may come about (hypothesis generation). The concept of decision trees is well suited for this.