# Decoding Drug Discovery: Exploring A-to-Z In silico Methods for Beginners

arXiv: https://arxiv.org/abs/2412.11137 · HTML: https://mlsanity.com/paper/2412.11137/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 259 references that are in the map.
- First posted on arXiv 15 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Learning to Plan Chemical Syntheses](https://mlsanity.com/paper/1708.04202/) (2017)
- [Learning Deep Architectures for Interaction Prediction in Structure-based Virtual Screening](https://mlsanity.com/paper/1610.07187/) (2016)
- [Application of machine learning for hematological diagnosis](https://mlsanity.com/paper/1708.00253/) (2017)
- [An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction](https://mlsanity.com/paper/1812.09073/) (2018)

## Abstract (arXiv)

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.