[In-Silico Drug Discovery Support-Current Situation and Challenges]

Area of Science:

• Computational chemistry and cheminformatics
• Drug discovery and development
• Bioinformatics and computational biology

Background:

• Traditional drug discovery faces challenges including high costs and slow development timelines.
• Depletion of target molecules and other factors impede efficient drug development.
• In silico drug discovery offers a promising solution to streamline the identification of novel drug targets and compounds.

Purpose of the Study:

• To provide an overview of in silico drug discovery methods, including ligand-based (LBDD) and structure-based (SBDD) approaches.
• To highlight the latest advancements in LBDD and SBDD, incorporating artificial intelligence (AI) and large-scale simulations.
• To present an example of in silico drug discovery support for identifying protein-protein interaction inhibitors in early-stage lung adenocarcinoma.

Main Methods:

• Ligand-based drug design (LBDD): Utilizes known ligand information based on structural and physicochemical properties.
• Structure-based drug design (SBDD): Employs the 3D structure of target proteins, applying the 'lock and key' principle.
• Integration of AI and large-scale simulations to enhance LBDD and SBDD methodologies.

Main Results:

• LBDD enables drug design when target protein structures are unknown, leveraging ligand similarities.
• SBDD facilitates the discovery of diverse compounds by targeting the 3D structure of proteins.
• AI and simulations are advancing the efficiency and scope of in silico drug design.

Conclusions:

• In silico drug discovery, encompassing LBDD and SBDD, is crucial for efficient drug development.
• AI and large-scale simulations represent the forefront of innovation in computational drug design.
• In silico methods show significant potential for identifying novel therapeutics, such as protein-protein interaction inhibitors for lung adenocarcinoma.