An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

MORF: Multi-view oblique random forest for hepatotoxicity prediction

Hepatotoxicity prediction is significant in drug development, so the experts expect to get effective and reliable references. Based on the consideration that the hepatotoxicity data involve multiple types of features, this paper proposes a multi-view oblique random forest (MORF) for hepatotoxicity prediction by considering each type of feature as an independent view. The Householder transformation is employed to get the inclined cut hyperplane in each view. Two versions of the multi-view oblique decision tree (ODT) algorithms were designed by generating optimal nodes based on selecting proper hyperplanes from different views, named ODT-N and ODT-R. These two types of ODT algorithms serve as the base learners to construct two MORF. Experiments conducted on the hepatotoxicity data provide performance comparisons among different algorithms, and the results confirm that our algorithms can fully utilize the information in different views.

StackDILI: Enhancing Drug-Induced Liver Injury Prediction through Stacking Strategy with Effective Molecular Representations

Drug-induced liver injury (DILI) is a major challenge in drug development, often leading to clinical trial failures and market withdrawals due to liver toxicity. This study presents StackDILI, a computational framework designed to accelerate toxicity assessment by predicting DILI risk. StackDILI integrates multiple molecular descriptors to extract structural and physicochemical features, including the constitution, pharmacophore, MACCS, and E-state descriptors. Additionally, a genetic algorithm is employed for feature selection and optimization, ensuring that the most relevant features are used. These optimized features are processed through a stacking ensemble model comprising multiple tree-based machine learning models, improving prediction accuracy and interpretability. Notably, StackDILI demonstrates a strong performance on the DILIrank test set and maintains robustness across cross-validation. Moreover, interpretability analysis reveals key molecular features associated with DILI risks, providing valuable insights into toxicity prediction. To further improve accessibility, a user-friendly web interface is developed, allowing users to input SMILES strings and receive rapid predictions with ease. The StackDILI model provides a powerful tool for efficient DILI assessment, supporting safer drug development. The web interface is accessible at https://awi.cuhk.edu.cn/biosequence/StackDILI/.

Deep Learning Prediction of Drug-Induced Liver Toxicity by Manifold Embedding of Quantum Information of Drug Molecules

PURPOSE

Drug-induced liver injury, or DILI, affects numerous patients and also presents significant challenges in drug development. It has been attempted to predict DILI of a chemical by in silico approaches, including data-driven machine learning models. Herein, we report a recent DILI deep-learning effort that utilized our molecular representation concept by manifold embedding electronic attributes on a molecular surface.

METHODS

Local electronic attributes on a molecular surface were mapped to a lower-dimensional embedding of the surface manifold. Such an embedding was featurized in a matrix form and used in a deep-learning model as molecular input. The model was trained by a well-curated dataset and tested through cross-validations.

RESULTS

Our DILI prediction yielded superior results to the literature-reported efforts, suggesting that manifold embedding of electronic quantities on a molecular surface enables machine learning of molecular properties, including DILI.

CONCLUSIONS

The concept encodes the quantum information of a molecule that governs intermolecular interactions, potentially facilitating the deep-learning model development and training.

Molecular designing of potential environmentally friendly PFAS based on deep learning and generative models

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are widely used across a spectrum of industrial and consumer goods. Nonetheless, their persistent nature and tendency to accumulate in biological systems pose substantial environmental and health threats. Consequently, striking a balance between maximizing product efficiency and minimizing environmental and health risks by tailoring the molecular structure of PFAS has become a pivotal challenge in the fields of environmental chemistry and sustainable development. To address this issue, a computational workflow was proposed for designing an environmentally friendly PFAS by incorporating deep learning (DL) and molecular generative models. The hybrid DL architecture MolHGT+ based on heterogeneous graph neural network with transformer-like attention was applied to predict the surface tension, bioaccumulation, and hepatotoxicity of the molecules. Through virtual screening of the PFAS master database using MolHGT+, the findings indicate that incorporating the siloxane group and betaine fragment can effectively decrease both the bioaccumulation and hepatotoxicity of PFAS while preserving low surface tension. In addition, molecular generative models were employed to create a structurally diverse pool of novel PFASs with the aforementioned hit molecules serving as the initial template structures. Overall, our study presents a promising AI-driven method for advancing the development of environmentally friendly PFAS.

Predicting drug-induced liver injury using graph attention mechanism and molecular fingerprints

Drug-induced liver injury (DILI) is a significant issue in drug development and clinical treatment due to its potential to cause liver dysfunction or damage, which, in severe cases, can lead to liver failure or even fatality. DILI has numerous pathogenic factors, many of which remain incompletely understood. Consequently, it is imperative to devise methodologies and tools for anticipatory assessment of DILI risk in the initial phases of drug development. In this study, we present DMFPGA, a novel deep learning predictive model designed to predict DILI. To provide a comprehensive description of molecular properties, we employ a multi-head graph attention mechanism to extract features from the molecular graphs, representing characteristics at the level of compound nodes. Additionally, we combine multiple fingerprints of molecules to capture features at the molecular level of compounds. The fusion of molecular fingerprints and graph features can more fully express the properties of compounds. Subsequently, we employ a fully connected neural network to classify compounds as either DILI-positive or DILI-negative. To rigorously evaluate DMFPGA's performance, we conduct a 5-fold cross-validation experiment. The obtained results demonstrate the superiority of our method over four existing state-of-the-art computational approaches, exhibiting an average AUC of 0.935 and an average ACC of 0.934. We believe that DMFPGA is helpful for early-stage DILI prediction and assessment in drug development.

GeoDILI: A Robust and Interpretable Model for Drug-Induced Liver Injury Prediction Using Graph Neural Network-Based Molecular Geometric Representation

Drug-induced liver injury (DILI) is a significant cause of drug failure and withdrawal due to liver damage. Accurate prediction of hepatotoxic compounds is crucial for safe drug development. Several DILI prediction models have been published, but they are built on different data sets, making it difficult to compare model performance. Moreover, most existing models are based on molecular fingerprints or descriptors, neglecting molecular geometric properties and lacking interpretability. To address these limitations, we developed GeoDILI, an interpretable graph neural network that uses a molecular geometric representation. First, we utilized a geometry-based pretrained molecular representation and optimized it on the DILI data set to improve predictive performance. Second, we leveraged gradient information to obtain high-precision atomic-level weights and deduce the dominant substructure. We benchmarked GeoDILI against recently published DILI prediction models, as well as popular GNN models and fingerprint-based machine learning models using the same data set, showing superior predictive performance of our proposed model. We applied the interpretable method in the DILI data set and derived seven precise and mechanistically elucidated structural alerts. Overall, GeoDILI provides a promising approach for accurate and interpretable DILI prediction with potential applications in drug discovery and safety assessment. The data and source code are available at GitHub repository (https://github.com/CSU-QJY/GeoDILI).