Perspective of CADD and AIDD in medicinal chemistry / 药学学报

Artificial intelligence-aided drug discovery (AIDD) is a new version of computer-aided drug discovery (CADD). AIDD is featured in significantly promoting the performance of conventional CADD. AI markedly enhances the learning ability of CADD. In the 1960s, CADD was established from conventional QSAR approaches, which mainly used regression approaches to derive substructure-activity relationship for compounds with a common scaffold, and guide drug molecular design, figure out the binding features of drugs, and identify potential drug targets. Since the 1990s, structural biology has provided three-dimensional structures of drug targets, enabling drug discovery based on target structure (SBDD), fragment-based drug discovery (FBDD), and structure-based virtual screening (SBVS) with CADD approaches. In the past 30 years, many first in class (FIC) and best in class (BIC) drugs were discovered with CADD. Now, AIDD will further revolutionize CADD by reducing human interventions and mining big chemical and biological data. It is expected that AIDD will significantly enhance the abilities of CADD, virtual screening and drug target identification. This article tries to provide perspectives of CADD and AIDD in medicinal chemistry with case studies.

Evolution of modern age drug discovery of lipopeptides and computer-aided drug discovery in India

The endeavor has been attempted to present a review on the evolution of modern age drug discovery in India. The contribution of next generation therapeutics options microbial metabolites and the computational drug discovery aspects to the global market from India have been represented. Microbial metabolites such as lipopeptides and peptide therapeutics are gaining worldwide importance due to their multiple applications as broad-spectrum antimicrobial, antiviral, anticancer properties etc. Due to the surge of microbial resistance, tumor resistance, and ongoing pandemic due to constantly mutating corona virus, there is a need to develop next-generation therapeutics options from natural origin, less toxic to the environment, and have higher specificity towards target. Small molecule therapeutics are certainly less specific towards cancer targets hence the cytotoxicity is a major issue in cancer treatment while drug resistance due to the mutations are coming as challenges every day for drug discovery researchers. Microbial lipopeptide reserves a sweet spot in between the small molecule inhibitors and peptide therapeutics because of their amphiphilic compounds consist of a fatty acid side chain and a cyclic peptide moiety of hydrophilic nature. The computational drug discovery approach accelerates the drug discovery process due to the advancement in supercomputer facilities provided by various funding agencies such as the Department of Biotechnology (DBT) and the Department of Science and Technology (DST) in India. The current review article is focusing light on the research contribution of Indian Scientists and Govt. of India in the field of lipopeptide-based research and applications of Computer-aided drug discovery.

A survey on computational methods for the classification of compounds into drug and non-drug / 国际药学研究杂志

The introduction of computer methods and machine learning has drastically changed the field of drug discovery. Now, drug designing has evolved from Drug Discovery into Computer Aided Drug Discovery (CADD). There are several reasons that led to this change. The low success rate of classification of compounds into drugs and non-drugs, being one of those reasons, has evoked a renewed interest in understanding more clearly what makes a compound drug-like and its new model and methods for classification. Our work reviews a number of works based on different methods and models of machine learning techniques for identifying and classifying drug-like molecules which ranges frombasicdescriptor-basedmethodtothelatestfingerprint-basedmethod.

Application of computational methods for anticancer drug discovery, design, and optimization / Aplicación de métodos computacionales para el descubrimiento, diseño y optimización de fármacos contra el cáncer

Abstract: Developing a novel drug is a complex, risky, expensive and time-consuming venture. It is estimated that the conventional drug discovery process ending with a new medicine ready for the market can take up to 15 years and more than a billion USD. Fortunately, this scenario has recently changed with the arrival of new approaches. Many novel technologies and methodologies have been developed to increase the efficiency of the drug discovery process, and computational methodologies have become a crucial component of many drug discovery programs. From hit identification to lead optimization, techniques such as ligand- or structure-based virtual screening are widely used in many discovery efforts. It is the case for designing potential anticancer drugs and drug candidates, where these computational approaches have had a major impact over the years and have provided fruitful insights into the field of cancer. In this paper, we review the concept of rational design presenting some of the most representative examples of molecules identified by means of it. Key principles are illustrated through case studies including specifically successful achievements in the field of anticancer drug design to demonstrate that research advances, with the aid of in silico drug design, have the potential to create novel anticancer drugs.

Resumen: El desarrollo de un nuevo fármaco es un proceso complejo y arriesgado que requiere una enorme cantidad de tiempo y dinero. Se estima que el proceso estándar para producir un nuevo fármaco, desde su descubrimiento hasta que acaba en el mercado, puede tardar hasta 15 años y tener un costo de mil millones de dólares (USD). Por fortuna, este escenario ha cambiado recientemente con la llegada de nuevas tecnologías y metodologías. Entre ellas, los métodos computacionales se han convertido en un componente determinante en muchos programas de descubrimiento de fármacos. En un esfuerzo por incrementar las posibilidades de encontrar nuevas moléculas con potencial farmacológico, se utilizan técnicas como el cribado virtual de quimiotecas construidas con base en ligandos o estructuras para la identificación de hits y hasta para la optimización de compuestos líder. En lo que respecta al diseño y descubrimiento de nuevos candidatos a fármacos contra el cáncer, estos enfoques tienen, a la fecha, un impacto importante y aportan nuevas posibilidades terapéuticas. En este artículo se revisa el concepto del diseño racional de moléculas con potencial farmacológico, ilustrando los principios clave con algunos de los ejemplos más representativos y exitosos de moléculas identificadas mediante estas aproximaciones. Se incluyen casos desarrollados en el campo del diseño de fármacos contra el cáncer con la finalidad de mostrar cómo, con la ayuda del diseño asistido por computadora, se pueden generar nuevos fármacos que den esperanza a millones de pacientes.