Decoding Drug Discovery: Exploring A-to-Z In silico Methods for Beginners
Rasul, Hezha O., Ghafour, Dlzar D., Aziz, Bakhtyar K., Hassan, Bryar A., Rashid, Tarik A., Kivrak, Arif
–arXiv.org Artificial Intelligence
The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional methods such as in vivo and in vitro approaches are limited in their ability to analyze vast amounts of data efficiently, leading to wasteful outcomes. To expedite and streamline drug development, an increasing reliance on computer-aided drug design (CADD) approaches has merged. These sophisticated in silico methods offer a promising avenue for efficiently identifying viable drug candidates, thus providing pharmaceutical firms with significant opportunities to uncover new prospective drug targets. The main goal of this work is to review in silico methods used in the drug development process with a focus on identifying therapeutic targets linked to specific diseases at the genetic or protein level. This article thoroughly discusses A-to-Z in silico techniques, which are essential for identifying the targets of bioactive compounds and their potential therapeutic effects. This review intends to improve drug discovery processes by illuminating the state of these cutting-edge approaches, thereby maximizing the effectiveness and duration of clinical trials for novel drug target investigation.
arXiv.org Artificial Intelligence
Dec-15-2024
- Country:
- Africa > Ethiopia (0.04)
- Asia
- Indonesia > Bali (0.04)
- Japan > Honshū
- Kansai > Wakayama Prefecture > Wakayama (0.04)
- Middle East
- Taiwan (0.04)
- Europe
- North America > United States (0.93)
- South America > Brazil (0.04)
- Genre:
- Overview (1.00)
- Research Report
- Experimental Study (1.00)
- New Finding (0.87)
- Workflow (1.00)
- Industry:
- Government > Regional Government
- North America Government > United States Government > FDA (0.67)
- Health & Medicine
- Pharmaceuticals & Biotechnology (1.00)
- Therapeutic Area
- Immunology (1.00)
- Infections and Infectious Diseases (1.00)
- Neurology (1.00)
- Obstetrics/Gynecology (0.67)
- Oncology (1.00)
- Government > Regional Government
- Technology:
- Information Technology
- Artificial Intelligence
- Machine Learning
- Neural Networks > Deep Learning (0.93)
- Statistical Learning (1.00)
- Natural Language (0.67)
- Representation & Reasoning > Search (0.67)
- Machine Learning
- Biomedical Informatics > Translational Bioinformatics (1.00)
- Data Science > Data Mining (1.00)
- Modeling & Simulation (0.93)
- Artificial Intelligence
- Information Technology