Drug Discovery Problem

The Drug Discovery Problem refers to the task of designing new molecular structures with potential therapeutic properties. It is a complex optimization problem that can be approached using computational search methods to explore a vast chemical space.

In this particular formulation, the optimization process is guided solely by the QED (Quantitative Estimate of Drug-likeness) coefficient, which is a numerical score estimating how “drug-like” a molecule is based on structural and physicochemical properties. The goal is to find molecules with the highest possible QED score.

Problem Definition

• Problem: Problem is represented with class DrugDiscoveryProblem.

• Instance: A set of candidate molecules (represented as SMILES strings) serving as the initial population for optimization.

• Solution: A molecular structure with the highest achievable QED coefficient within the search process.

• Measure: Maximize the QED coefficient, a real number between 0 and 1, where higher values indicate greater drug-likeness.

Applications

While real-world drug discovery involves multiple evaluation criteria, optimizing solely for QED can serve as a simplified proof-of-concept or benchmark for molecular optimization algorithms. This approach can be used in educational, experimental, or early prototyping scenarios before introducing additional objectives.