Software Infrastructure for Computer-aided Drug Discovery and Development, a Practical Example with Guidelines.

Area of Science:

• Computational chemistry
• cheminformatics
• Drug discovery and development

Background:

• Effective software infrastructure is crucial for computer-aided drug discovery and development (CADDD).
• Existing computational tools often require significant user expertise in file formatting and workflow management.
• A unified and efficient system can enhance productivity in in silico drug design.

Purpose of the Study:

• To present a novel software solution for integrating standard computational services in CADDD.
• To empower users of varying expertise levels to conduct in silico experiments seamlessly.
• To demonstrate the system's capability in managing computations, tracking activities, and rendering structural outcomes.

Main Methods:

• Development of a functional unit integrating diverse computational services.
• Implementation of automated workflows to handle file formatting and software compatibility.
• Inclusion of features for computation management, activity tracking, and graphical rendering.
• Performance evaluation of five distinct molecular docking programs using a Human Immunodeficiency Virus-1 (HIV-1) protease dataset.

Main Results:

• The developed system successfully integrates standard computational services for drug discovery.
• Users can execute in silico experiments automatically, irrespective of their technical expertise.
• The system manages computational tasks, tracks experimental progress, and visualizes structural results effectively.
• Comparative analysis of five docking programs on the HIV-1 protease test set was successfully performed.

Conclusions:

• The presented software system significantly enhances the efficiency and accessibility of CADDD.
• This integrated approach simplifies complex in silico workflows, facilitating broader adoption.
• The system demonstrates the potential for streamlined computational modeling in drug discovery research.