Q-Drug: a Framework to bring Drug Design into Quantum Space using Deep Learning

TL;DR

Q-Drug introduces a quantum-inspired deep learning framework for molecular optimization, encoding molecules into binary embeddings, optimizing with quantum algorithms, and outperforming existing methods in drug property enhancement.

Contribution

The paper presents a novel quantum-inspired optimization framework for drug molecules using binary embeddings and quantum algorithms, enabling faster and more effective molecular property optimization.

Findings

Outperforms existing molecular optimization methods

Finds better molecules in 1/20th to 1/10th of the time

Compatible with various quantum computing hardware

Abstract

Optimizing the properties of molecules (materials or drugs) for stronger toughness, lower toxicity, or better bioavailability has been a long-standing challenge. In this context, we propose a molecular optimization framework called Q-Drug (Quantum-inspired optimization algorithm for Drugs) that leverages quantum-inspired algorithms to optimize molecules on discrete binary domain variables. The framework begins by encoding the molecules into binary embeddings using a discrete VAE. The binary embeddings are then used to construct an Ising energy-like objective function, over which the state-of-the-art quantum-inspired optimization algorithm is adopted to find the optima. The binary embeddings corresponding to the optima are decoded to obtain the optimized molecules. We have tested the framework for optimizing drug molecule properties and have found that it outperforms other molecular optimization methods, finding molecules with better properties in 1/20th to 1/10th of the time previously required. The framework can also be deployed directly on various quantum computing equipment, such as laser pulses CIMs, FPGA Ising Machines, and quantum computers based on quantum annealing, among others. Our work demonstrates a new paradigm that leverages the advantages of quantum computing and AI to solve practically useful problems.