arXiv subjects

Morteza Rasoulianboroujeni

Publications and source records attributed to Morteza Rasoulianboroujeni.

5 recordsLinked to original sources

arXiv2026

Decoding Extrahepatic Targeting of Lipid Nanoparticles with Interpretable Machine Learning

Lipid nanoparticles (LNPs) have transformed RNA medicine, yet their clinical utility remains constrained by predominant hepatic accumulation after systemic administration. Redirecting LNPs to extrahepatic tissues requires understanding of how lipid chemistry and formulation composition jointly govern in vivo biodistribution. Here, we develop an interpretable machine learning framework to predict hepatic versus extrahepatic LNP accumulation and identify molecular design rules for extrahepatic RNA delivery. A literature-derived dataset of 476 intravenous LNP formulations was curated from 81 studies, integrating formulation composition, lipid chemical structures, and IVIS-based biodistribution profiles. Standardized SMILES representations of ionizable lipids, helper lipids, sterols, PEGylated or polymer-conjugated lipids, additional lipids, and polymer repeat units were converted into RDKit Expert descriptors and combined with formulation-level variables to generate an 808-dimensional feature representation. Logistic regression, random forest, and XGBoost achieved ROC-AUC values of 0.839, 0.866, and 0.874, respectively. SHAP-based interpretation and consensus feature ranking revealed that ionizable-lipid descriptors dominate biodistribution prediction, while formulation composition, particularly ionizable lipid, sterol, and PEGylated/polymer-conjugated lipid fractions, contributes substantially. The top 20 consensus features retained nearly all predictive information in tree-based models. The most informative features implicated electrotopological surface properties, charge- and hydrophobicity-weighted surface areas, molecular topology, and amide/alkyl structural motifs as drivers of extrahepatic accumulation. This study establishes an interpretable, data-driven strategy for decoding LNP biodistribution and provides actionable design principles for engineering LNPs beyond the liver.

q-bio.QM↗

arXiv2026

A Machine Learning Benchmarking Framework for Lipid Nanoparticle Transfection Efficiency Prediction

The discovery of new ionizable lipids for efficient lipid nanoparticle (LNP)-mediated RNA delivery remains a major bottleneck in RNA therapeutics development. Recent advances demonstrate the potential of machine learning (ML) models to predict transfection efficiency directly from lipid structure, enabling high-throughput virtual screening and accelerating lead identification. However, as new models for LNP transfection prediction continue to emerge, the lack of rigorous and standardized benchmarking poses a significant risk and may undermine confidence in their reliability for discovery. Here, we present a robust ML benchmarking framework for evaluating transfection prediction models based on ionizable lipid structures. This framework systematically benchmarks diverse molecular representations paired with a broad range of ML architectures spanning traditional models, feedforward neural networks, and state-of-the-art graph-based methods. In addition, the presented framework supports assessment of model generalization and evaluates prediction reliability beyond standard regression metrics. Using a curated dataset of 1,100 unique ionizable lipid structures derived from the HeLa transfection dataset originally reported by Xu et al., we show that within this framework, models leveraging explicit molecular substructure encoding consistently achieve the highest predictive accuracy and should serve as essential baselines for the development of new, more sophisticated models. In contrast, some current graph-based models, including AGILE, Chemprop, and KPGT, tend to show comparatively lower accuracy. The presented framework provides a standardized, transparent, and comprehensive benchmarking resource that enables meaningful comparison of emerging architectures and establishes strong baselines for future development of predictive models in lipid-based RNA delivery.

q-bio.QM↗

arXiv2024

Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach

Ionizable lipids are essential in developing lipid nanoparticles (LNPs) for effective messenger RNA (mRNA) delivery. While traditional methods for designing new ionizable lipids are typically time-consuming, deep generative models have emerged as a powerful solution, significantly accelerating the molecular discovery process. However, a practical challenge arises as the molecular structures generated can often be difficult or infeasible to synthesize. This project explores Monte Carlo tree search (MCTS)-based generative models for synthesizable ionizable lipids. Leveraging a synthetically accessible lipid building block dataset and two specialized predictors to guide the search through chemical space, we introduce a policy network guided MCTS generative model capable of producing new ionizable lipids with available synthesis pathways.

cs.LG↗

arXiv2024

A Deep Generative Model for the Design of Synthesizable Ionizable Lipids

Lipid nanoparticles (LNPs) are vital in modern biomedicine, enabling the effective delivery of mRNA for vaccines and therapies by protecting it from rapid degradation. Among the components of LNPs, ionizable lipids play a key role in RNA protection and facilitate its delivery into the cytoplasm. However, designing ionizable lipids is complex. Deep generative models can accelerate this process and explore a larger candidate space compared to traditional methods. Due to the structural differences between lipids and small molecules, existing generative models used for small molecule generation are unsuitable for lipid generation. To address this, we developed a deep generative model specifically tailored for the discovery of ionizable lipids. Our model generates novel ionizable lipid structures and provides synthesis paths using synthetically accessible building blocks, addressing synthesizability. This advancement holds promise for streamlining the development of lipid-based delivery systems, potentially accelerating the deployment of new therapeutic agents, including mRNA vaccines and gene therapies.

cs.LG↗

arXiv2020

Magnon and Spin Transition Contribution in Heat Capacity of Ferromagnetic Cr-doped MnTe: Experimental Evidence for a Paramagnetic Spin-Caloritronic Effect

We present experimental evidence for the simultaneous existence of the magnons and spin-state transition contributions to the heat capacity in ferromagnetic (FM) Cr-doped MnTe (Tc~280K), where the magnon heat capacity is attributed to the observed magnon-bipolar carrier-drag thermopower. The pristine antiferromagnetic (AFM) MnTe shows only a magnon-induced peak in the heat capacity near the Neel temperature, TN~307K. However, Cr-doped MnTe shows a magnon-contributed heat capacity peak at ~293K with an additional peak in the deep paramagnetic domain near 780K. Temperature-dependent magnetic susceptibility reveals that Cr-doping initially creates low-spin (LS) states Mn2+ ions into MnTe near and below TN due to a higher crystal field induced by Cr ions. Above 400K, LS Mn2+ ions start converting into high-spin (HS) Mn2+ ions. The LS-to-HS transition of Mn2+ leads to an excess entropy and hence excess heat capacity contribution in the system. Temperature-dependent X-ray diffraction (XRD) and magnetic field-dependent susceptibility (M-H) confirmed no presence of any structural changes and magnetic polaron, respectively. Both XRD and M-H ensure that the peak of the heat capacity in the paramagnetic domain is originated solely by the spin-state transition. The heat capacity versus temperature was calculated to explain the contribution of each component, including the ones due to the phonons, magnons, spin-transition, Schottky anomaly, and lattice dilation. With the recent advances in spin-caloritronics extending the spin-based effects from magnetic to paramagnetic materials, the data from the heat capacity can play a crucial role to probe the presence of different phenomena such as paramagnon-carrier-drag and spin-entropy thermopowers.

physics.app-ph↗