Nano-based drug delivery systems present a promising approach to improve the efficacy and safety of therapeutics by enabling targeted drug transport and controlled release. In parallel, computational approaches—particularly Molecular Dynamics (MD) simulations and Artificial Intelligence (AI)—have emerged as transformative tools to accelerate nanocarrier design and optimise their properties. MD simulations provide atomic-to-mesoscale insights into nanoparticle interactions with biological membranes, elucidating how factors such as surface charge density, ligand functionalisation and nanoparticle size affect cellular uptake and stability. Complementing MD simulations, AI-driven models accelerate the discovery of lipid-based nanoparticle formulations by analysing vast chemical datasets and predicting optimal structures for gene delivery and vaccine development. By harnessing these computational approaches, researchers can rapidly refine nanoparticle composition to improve biocompatibility, reduce toxicity and achieve more precise drug targeting. This review synthesises key advances in MD simulations and AI for two leading nanoparticle platforms (gold and lipid nanoparticles) and highlights their role in enhancing therapeutic performance. We evaluate how in silico models guide experimental validation, inform rational design strategies and ultimately streamline the transition from bench to bedside. Finally, we address key challenges such as data scarcity and complex in vivo dynamics and propose future directions for integrating computational insights into next generation nanodelivery systems.

Quantitative Structure Activity Relationship (QSAR) remains an effective tool for early-stage chemical modelling and virtual screening in drug design. The advancements in this field are led by two core paradigms, 1) descriptor engineering, where complex fixed-length vectors of compounds are generated and conventional ML methods are applied to those representations and 2) raw chemical inputs (e.g., SMILES, 2D-graph) are provided to complex deep learning neural network models which construct their own internal representations of molecules to quantify their biological activity. Conventional ML uses simple, computationally efficient algorithms and achieves state of the art accuracy on dataset sizes typical of practical QSAR applications. NN-based ML may not be as accurate for these dataset sizes but is inherently flexible and can learn iteratively. Here we present the Tsetlin Machine as a new potential QSAR method - which combines the accuracy and easy-use of existing rule-based QSAR ML methods (e.g., RF and XGBoost) with the continuous learning aspect and flexibility of NN algorithms. The Tsetlin Machine uses teams of finite-state automata which capture frequent patterns as propositional logic (clauses) in a reinforcement learning based game. We compare the TM's performance to existing rule-based QSAR methods using a benchmarking pipeline developed by the Polaris group. It uses repeated measures ANOVA and Tukey’s HSD to measure the difference in mean performance distributions. Across all tested targets, compound groups and descriptor types, the TM consistently achieved high ROC AUC and PPV scores - making it well-suited for QSAR-based compound prioritization, especially in early stages of virtual screening pipelines. SOTA results were achieved with the TM for MOR (ROC-AUC = 0.87, PRC-AUC = 0.77) and CYPA4 (ROC-AUC = 0.92, PRC-AUC = 0.63), demonstrating comparable and in certain cases superior performance of TM over the established SOTA models. Additionally, this work contains practical guidelines for applying TMs to QSAR tasks including descriptor selection, model parameters and number of training epochs that were used to produce the reported SOTA results.

Advances in Quantitative Structure Activity Relationship (QSAR) are led by two core paradigms, (1) descriptor engineering, where complex fixed-length vectors of compounds are generated and conventional ML methods are applied to those representations and (2) graphical chemical inputs (e.g., Simplified Molecular Input Line Entry System (SMILES), 2D-graph) being provided to deep learning neural network (NN) models, which construct their own internal representations of molecules and learn iteratively over them. Here we present the Tsetlin Machine (TM)─which combines the accuracy and easy-use of existing rule-based QSAR ML methods (e.g., RF and XGBoost), the iterative learning aspect of NN algorithms and its intrinsic interpretability. The TM uses teams of finite-state automata which capture frequent patterns as propositional logic (clauses) via reinforcement learning. The benchmarking pipeline presented here demonstrates that TM-QSAR coupled with ECFP4 descriptors frequently performs better than existing rule-based QSAR methods for ROC-AUC, PRC-AUC and PPV, with a high capacity for interscaffold generalization. However, due to the binary nature of TM-QSAR, performance it is currently limited when descretised continuous descriptors are used. TM-QSAR demonstrated particularly impressive classification scores for MOR (ROC-AUC = 0.87, PRC-AUC = 0.77) and CYPA4 (ROC-AUC = 0.92, PRC-AUC = 0.63), when compared to RF and XGBoost. Using TM in combination with substructural fingerprinting descriptors allows for an interpretability suite which can be extracted directly from clauses. Here we detail molecule property maps (TM-MPM) to view atom-wise TM-QSAR bioactivity contributions for single molecules and closed-form WAC scores (Weights × Activations × Clauses) for descriptor-wise contributions to regions of predicted chemical space. These methods show strong alignment of TM-QSAR interpretations to known ligand-protein interactions of the MOR target and gives nonlinear, conditional interpretations for greater predicted bioactivity. Given this combination of accuracy, computational efficiency and interpretability, we provide a basis for TM-QSAR to be explored as a standard methodology in virtual screening toolkits.