The world today, although, has developed an elaborate health system to fortify against known and unknown diseases, it continues to be challenged by new as well as emerging, and re-emerging infectious disease threats with severity and probable fluctuations. These threats also have varying costs for morbidity and mortality, as well as for a complex set of socio-economic outcomes. Some of these diseases are often caused by pathogens which use humans as host. In such cases, it becomes paramount responsibility to dig out the source of pathogen survival to stop their population growth. Sequencing genomes has been finessed so much in the 21st century that complete genomes of any pathogen can be sequenced in a matter of days following which; different potential drug targets are needed to be identified. Structure modeling of the selected sequences is an initial step in structure-based drug design (SBDD). Dynamical study of predicted models provides a stable target structure. Results of these in-silico techniques greatly depend on force field (FF) parameters used. Thus, in this chapter, we intend to discuss the role of FF parameters used in protein structure prediction and molecular dynamics simulation to provide a brief overview on this area.
Part of the book: Homology Molecular Modeling
Malaria, the severe vector-borne disease has embedded serious consequences on mankind since ages, causing deterioration of health, leading to deaths. The causative parasite has a wide distribution aligned from tropical to subtropical regions. Out of all the five species Plasmodium vivax and Plasmodium falciparum have registered about more than 600 million cases worldwide. Throughout the decades, identification of various antimalarial drugs, targets, preventive measures and advancement of vaccines were achieved. The key to executing malaria elimination is the appropriate laboratory diagnosis. Development includes positive scientific judgments for a vaccine, advanced progress of 3 non-pyrethroid insecticides, novel genetic technologies, possibilities to alter malaria parasite mediation by the mosquito, identification of drug resistance markers, initiation of Plasmodium vivax liver stage assessment, perspective to mathematical modeling and screening for active ingredients for drugs and insecticides. Although the last century witnessed many successful programs with scientific progress, however, this was matched with notable obstacles. The mutation in the genes has changed the overall gameplay of eradication. This chapter aims to examine the numerous experimental and theoretical works that have been established in the last two decades along with the ongoing methodologies consisting of detailed explanations necessary for the establishment of new targets and drugs.
Part of the book: Current Topics and Emerging Issues in Malaria Elimination
In recent years, the pharmaceutical business has seen a considerable increase in data digitization. With digitization, however, comes the challenge of obtaining, analyzing, and applying knowledge to solve complex clinical problems. Artificial intelligence (AI), which entails a variety of advanced tools and networks that can mimic human intellect, can overcome such challenges with traditional pharmaceutical development. Artificial intelligence and machine learning have a vast role in therapeutic development, including the prediction of drug target and properties of small molecules. By predicting the 3D protein structure, AI techniques, such as Alpha Fold, can help with structure-based drug development. Machine learning algorithms have been utilized to anticipate the properties of small molecules based on their chemical structure. Many researches have shown the importance of using in silico predictive ADMET (absorption, distribution, metabolism, excretion, and toxicity) models to speed up the discovery of small compounds with enhanced efficacy, safety, and dosage. This chapter discusses various roles of these methods in the development of effective therapeutics.
Part of the book: Drug Development Life Cycle