Nanotechnology Forecast Models

Modeling nanoparticle diffusion using physics-informed generative AI Accurately capturing the dynamics of nanoparticles in complex liquid environments is critical for understanding interactions at the nanoscale. Liquid-phase Transmission Electron Microscopy (LPTEM) enables direct observation of these dynamics, yet decoding the underlying physical mechanisms remains challenging due to stochastic nanoparticle motion and complex interaction landscapes. In a recent study published in Nature Communications, Zain Shabeeb and coauthors present LEONARDO, a physics-informed generative model combining a variational autoencoder (VAE) with a transformer-based neural network architecture. This model employs a specialized physics-informed loss function to learn essential statistical features of nanoparticle trajectories obtained from LPTEM, including displacement distributions, non-Gaussian behavior, and temporal autocorrelations. LEONARDO demonstrates accurate reconstruction and generation of nanoparticle trajectories, effectively modeling diffusion characteristics indicative of environmental heterogeneity and viscoelastic effects. This work provides a robust computational approach for interpreting nanoparticle behavior observed in electron microscopy experiments, contributing significantly to our understanding of nanoscale dynamics and interactions. Paper: https://lnkd.in/duDkBGT6 #GenerativeAI #NanoparticleDiffusion #LiquidPhaseTEM #MachineLearning #VariationalAutoencoder #TransformerModel #PhysicsInformedML #Nanotechnology #ElectronMicroscopy #NatureCommunications #AIforScience #MaterialsScience #Research

The Nature Nanotechnology article “Designing lipid nanoparticles using a transformer-based neural network” introduces COMET, a transformer model that predicts LNP efficacy end-to-end by encoding lipid structures, compositions, and formulation parameters. The authors built LANCE, a large dataset spanning >6,000 LNPs across identities, ratios, and synthesis conditions using FLuc mRNA. COMET generalizes beyond training by handling dual-ionizable-lipid and polymer-containing formulations, multitask settings across cell types, and small-data regimes; it also predicts lyophilization stability. Experimental screens identified COMET-prioritized LNPs achieving strong protein expression in vitro and in vivo, suggesting accelerated, data-efficient optimization of nucleic-acid delivery formulations for therapeutics and manufacturing applications. Congrats to Alvin and his co-workers. https://lnkd.in/e4KdJ5gv

Interesting paper on integrating machine learning into design of polymeric nanoparticles published in AAPS PharmSciTech. Integrating machine learning (ML) into nanotechnology represents a promising strategy for rational design and accelerated development of drug delivery systems. However, studies in this field are scarce and face methodological and interpretative problems. This study presents a modular ML pipeline for the predictive modeling of nanoparticles produced via nanoprecipitation using isoniazid as a model drug. The workflow was structured into three sequential steps: (1) binary classification to predict nanoparticle formation, (2) multiclass classification to estimate size ranges, and (3) regression to refine size prediction. Several algorithms were used, including Extreme Gradient Boosting, Random Forest, Artificial Neural Networks (ANN), Generalized Linear Models, and Naive Bayes. A total of 90 formulations were evaluated over three iterative experimental rounds. In each cycle, models were retrained with new data and used to simulate virtual formulations, thereby guiding the selection of experiments to reduce data imbalance and improve prediction accuracy. The ANN algorithm consistently outperformed other models in all steps, achieving an R2 > 0.9 in both classification and regression tasks. Classification outputs were used as constraints in the regression phase to improve robustness. The final pipeline demonstrated high predictive performance across a broad size range (75–768 nm), with maximum absolute errors below 40 nm. Validation with new formulations confirmed the model's reliability and generalization capacity. This approach may significantly reduce experimental workload while providing a scalable framework. Overall, the proposed ML-guided strategy supports data-driven decision-making in nanopharmaceutical research, enabling systematic formulation development aligned with Quality-by-Design principles. @rodrigo Fonseca silveira @ingrid de Santana Ana Luiza Lima Idejan P. Gross Tais Guilherme M. Gelfuso Marcílio Cunha Filho American Association of Pharmaceutical Scientists (AAPS) | @aapscomms Miguel O. Jara Claudio Salomon Michael Repka QI (Tony) ZHOU Sanyog Jain