Surrogate Models and Global Optimization in Chemistry: Methodological Innovation in Drug Discovery

This article focuses on the application of surrogate model optimization and global optimization methods in chemistry and drug discovery, aiming to address the core challenges of high computational costs and vast chemical spaces in drug development. It covers key technologies such as Bayesian optimization, Gaussian processes, and genetic algorithms, as well as cutting-edge directions like multi-fidelity modeling and deep generative models, exploring their principles, implementations, and application prospects to provide insights for the intelligent transformation of drug discovery.

In the field of drug discovery, researchers face a fundamental contradiction: precise molecular simulation and experimental validation consume enormous resources, while the chemical space of potential drug candidates is extremely vast, making traditional exhaustive search ineffective. This "curse of dimensionality" turns chemical optimization into a typical "black-box" challenge—only a small number of expensive high-fidelity evaluation results can be obtained within a limited budget. The rise of surrogate models and global optimization methods provides a systematic solution to this dilemma.

Surrogate model optimization replaces expensive real evaluations by constructing low-cost approximate models. Bayesian optimization is the mainstream paradigm: it uses Gaussian Processes (GP) as probabilistic surrogate models, providing predictive distributions instead of single values, and balances exploration and exploitation with acquisition functions (Expected Improvement EI, Probability of Improvement PI, Upper Confidence Bound UCB). In addition, Radial Basis Function Networks (RBFN) are suitable for multi-modal non-smooth potential energy surfaces, and neural networks (especially deep architectures) can automatically learn molecular representations, reducing reliance on manual feature engineering.

Global optimization aims to find the global optimal solution and avoid local optima. The evolutionary algorithm family includes: Genetic Algorithms (simulating natural selection, encoding molecular structures such as SMILES), Particle Swarm Optimization (drawing on bird flock behavior, sharing individual and group information), and Simulated Annealing (allowing acceptance of inferior solutions to escape local optima, with temperature parameters controlling the search phase). Markov Chain Monte Carlo (MCMC) constructs Markov chains for sampling, suitable for scenarios requiring uncertainty quantification such as conformational space exploration.

Drug discovery has fidelity levels (e.g., molecular force fields are fast but low-precision, DFT is high-precision but time-consuming). Multi-fidelity optimization leverages this structure: using low-fidelity to screen candidates and high-fidelity to evaluate a few schemes. Information fusion mechanisms include Co-Kriging (extending GP to establish data associations) and Deep Multi-Fidelity Networks (sharing representation layers to learn cross-fidelity features). Adaptive resource allocation is based on information gain criteria, dynamically determining the use of fidelity to maximize learning effects within a limited budget.

Deep generative models open up a new paradigm: learning implicit representations of molecular distributions and proactively proposing novel structures. Variational Autoencoders (VAE) encode molecules into a continuous latent space, decode into valid structures after optimization, and convert combinatorial search into continuous optimization. Diffusion models generate high-quality molecules through step-by-step denoising, which can be guided toward specific properties when combined with reinforcement learning; inverse molecular design directly maps target properties to structures, subverting the "generate-test" cycle.

Surrogate model optimization faces challenges in drug discovery deployment: 1. Validation and benchmarking: A fair and comprehensive benchmark system (covering chemical space, property targets, etc.) and statistical significance tests are needed; 2. Constraint handling and multi-objective trade-offs: Constraints such as synthetic accessibility and ADMET need to be handled, and multi-objective optimization requires Pareto frontier trade-offs; 3. Interpretability: Black-box models lack interpretability, requiring the embedding of physicochemical knowledge or the development of post-hoc explanation tools.

Surrogate models and global optimization are reshaping the computational paradigm of drug discovery. From Gaussian processes to deep generative models, from single-objective to multi-fidelity strategies, the technology stack is becoming increasingly mature. In the future, automated laboratories, high-throughput computing, and AI will be deeply integrated, and the vision of "autonomous drug discovery" will gradually be realized. Mastering these methods is key for chemists and researchers to enhance their technical capabilities and participate in the next generation of scientific discoveries.