Computer-aided Drug Design (CADD) approaches are essential in the drug discovery and development process. Pharmaceutical and biotechnology organizations, as well as academic institutions, utilize CADDs to identify and enhance the efficacy of bioactive compounds. This study aims to entice researchers by investigating the significance or value of Computer- aided Drug and Design (CADD) and its fundamental principles. The main focus is to speed up the drug discovery process, improve accuracy, and reduce the time and financial resources needed, ultimately making a positive impact on public health. A comprehensive literature search was conducted using databases such as PubMed and Scopus, focusing on studies published till 2024. The selection of studies was based on their analysis of the connection between contemporary pharmaceutical research and computer-aided drug design, with a focus on both structure-based and ligand-based drug design strategies can include molecular docking, fragment-based drug discovery, de novo drug design, pharmacophore modelling, Quantitative structure-activity relationship, 3D-QSAR, homology modelling, in silico absorption-distribution- metabolism-excretion-toxicity, and machine learning/deep learning. Computer-aided Drug Design (CADD) approaches are mathematical tools used to modify and measure certain characteristics of possible drug candidates. These methods are implemented in various applications. These encompass a variety of software products that are accessible to the public and can be purchased for corporate use. The CADD method is used at several stages of the drug development process, including as a foundation for chemical synthesis and biological testing. It provides information for the development of future SAR (Structure-Activity Relationship), resulting in enhanced molecules in terms of their activity and ADME (Absorption, Distribution, Metabolism, and Excretion). CADD techniques are predominantly employed to analyze and assess the affinity of large molecules for specific biomolecules, such as DNA, RNA, proteins, and enzymes, which serve exclusively as receptors. CADD improves the selection of lead compounds by predicting various parameters, including drug-likeness, physicochemical properties, pharmacokinetics, and toxicity. The application of CADD in drug modelling is to tackle challenges such as cost and time constraints. Modern computer-assisted drug discovery necessitates conducting virtual screening and high-throughput screening (HTS). Computer-aided drug design plays a crucial role for academic institutions and leading pharmaceutical companies in the development of drugs that enhance potency with the significance of reducing both time and costs.

In the dynamic landscape of drug discovery, Computer-Aided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADDs historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADDs predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open-Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. This paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

Contiguous ABCD1/ DXS1357E deletion syndrome (CADDS) is a rare deletion syndrome involving two contiguous genes on Xq28, ABCD1 and BCAP31 (formerly known as DXS1357E). Only nine individuals with this diagnosis have been reported in the medical literature to date. Intragenic loss-of-function variants in BCAP31 cause the deafness, dystonia, and cerebral hypomyelination syndrome (DDCH). Isolated pathogenic intragenic variants in ABCD1 are associated with the most common peroxisomal disorder, X-linked adrenoleukodystrophy (X-ALD), a single transporter deficiency, which in its more severe cerebral form is characterised by childhood-onset neurodegeneration and high levels of very-long-chain fatty acids (VLCFA). While increased VLCFA levels also feature in CADDS, the few patients described to date all presented as neonates with a severe phenotype. Here we report a tenth individual with CADDS, a male infant with dysmorphic facial features who was diagnosed through ultra-rapid whole genome sequencing (WGS) in the setting of persistent cholestatic liver disease, sensorineural hearing loss, hypotonia and growth failure and developmental delay. Biochemical studies showed elevated VLCFA and mildly reduced plasmalogens. He died at 7 months having developed pancreatic exocrine deficiency and interstitial lung disease, two features we propose to be possible extensions to the CADDS phenotype. We also review the genetic, phenotypic, and biochemical features in previously reported individuals with CADDS.

The era of drug design aided by computers has been very welcome to Indonesian researchers working on drug discovery and its related fields. The availability of software, bioinformatics, and cheminformatics data that are free and friendly to use increases the interest in carrying the software, bioinformatics, and cheminformatics out in drug discovery projects. The computational method is believed to speed up the discovery of new drugs, because the computational method can predict the molecular behavior of chemical compounds to interact with biological systems. Therefore, the real experimental study cost should be minimized, since there are a few promising molecules for a drug in silico only that would be investigated further. This article describes a decade of research concerning drug design and discovery aided by computers in Indonesia from 2011 to 2020. A number of computer-aided drug design-related articles (CADDs) from Indonesian journals were collected during the decade, and their trend was analyzed. The results show that there was an increasing trend in the use of CADD programs by Indonesian researchers during those ten years. The most common methods being used were docking and molecular dynamics with AutoDock/AutoDock Vina and AMBER/GROMACS, respectively. In addition, the most common protein targets, ligand structures, and disease targets being studied were human estrogen receptor alpha (HERa), flavonoids, and cancer.

The most common diagnoses after childhood sexual abuse are Post-Traumatic Stress Disorder and depression. The aim of this study is to design a decision support system to help psychiatry physicians in the treatment of childhood sexual abuse. Computer aided decision support system (CADSS) based on ANN, which predicts the development of PTSD and Major Depressive Disorder, using different parameters of the act of abuse and patients was designed. The data of 149 girls and 21 boys who were victims of sexual abuse were included in the study. In the designed CADDS, the gender of the victim, the type of sexual abuse, the age of exposure, the duration until reporting, the time of abuse, the proximity of the abuser to the victim, number of sexual abuse, whether the child is exposed to threats and violence during the abuse, the person who reported the event, and the intelligence level of the victim are used as input parameters. The average accuracy values for all three designed systems were calculated as 99.2%. It has been shown that the system designed by using these data can be used safely in the psychiatric assessment process, in order to differentiate psychiatric diagnoses in the early post-abuse period.