DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19

NFFGRAM: Nonlinear Multi-Feature Fusion and Gated Recurrent Self-Attention Mechanism for Traditional Chinese Medicine Formula Recommendation

Traditional Chinese Medicine (TCM) prescriptions are derived from the distinctive thought process and clinical experiences of Chinese medical theory. With the advent of artificial intelligence (AI), there is an enhanced ability to formulate these prescriptions by analyzing symptom data. However, the inherent sparseness of herb-symptom association data still limits the efficacy of such predictive methods. This study introduces an enhanced bipartite graph diffusion algorithm coupled with a gated recurrent self-attention mechanism for predicting herb and symptom associations. The initial phase involves the reconstruction of the herb-symptom association matrix, leveraging the fractal-weighted K-nearest neighbor algorithm. Subsequently, a method is conceived to extract analogous features between herbs and symptoms, which integrates linear neighborhood similarity with Gaussian kernel similarity, both based on fractal dimensions. The next stage employs a modified bipartite graph diffusion to deduce underlying herb-symptom relationships. This process culminates with the integration of the gated recurrent self-attention mechanism and a confidence scoring system to refine the herb-symptom association predictive matrix at a granular level. We benchmark our results against leading-edge algorithms to ascertain the precision and reliability of our model. Such as improvements of precision@20 by 21.77%, recall@20 by 12.46%, and F1-score@20 by 19.28% compared with the best baseline for the TCM2 dataset. Additionally, comprehensive case studies are undertaken, evaluating recommended prescriptions using insights from contemporary medicine and network pharmacology. The proposed model provides a novel paradigm for enhancing herbal prescription methodologies and TCM herb-based treatments.

A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods

Computational drug repositioning, which involves identifying new indications for existing drugs, is an increasingly attractive research area due to its advantages in reducing both overall cost and development time. As a result, a growing number of computational drug repositioning methods have emerged. Heterogeneous network-based drug repositioning methods have been shown to outperform other approaches. However, there is a dearth of systematic evaluation studies of these methods, encompassing performance, scalability and usability, as well as a standardized process for evaluating new methods. Additionally, previous studies have only compared several methods, with conflicting results. In this context, we conducted a systematic benchmarking study of 28 heterogeneous network-based drug repositioning methods on 11 existing datasets. We developed a comprehensive framework to evaluate their performance, scalability and usability. Our study revealed that methods such as HGIMC, ITRPCA and BNNR exhibit the best overall performance, as they rely on matrix completion or factorization. HINGRL, MLMC, ITRPCA and HGIMC demonstrate the best performance, while NMFDR, GROBMC and SCPMF display superior scalability. For usability, HGIMC, DRHGCN and BNNR are the top performers. Building on these findings, we developed an online tool called HN-DREP (http://hn-drep.lyhbio.com/) to facilitate researchers in viewing all the detailed evaluation results and selecting the appropriate method. HN-DREP also provides an external drug repositioning prediction service for a specific disease or drug by integrating predictions from all methods. Furthermore, we have released a Snakemake workflow named HN-DRES (https://github.com/lyhbio/HN-DRES) to facilitate benchmarking and support the extension of new methods into the field.

Predicting Drug-Disease Associations Through Similarity Network Fusion and Multi-View Feature Projection Representation

Predicting drug-disease associations (DDAs) through computational methods has become a prevalent trend in drug development because of their high efficiency and low cost. Existing methods usually focus on constructing heterogeneous networks by collecting multiple data resources to improve prediction ability. However, potential association possibilities of numerous unconfirmed drug-related or disease-related pairs are not sufficiently considered. In this article, we propose a novel computational model to predict new DDAs. First, a heterogeneous network is constructed, including four types of nodes (drugs, targets, cell lines, diseases) and three types of edges (associations, association scores, similarities). Second, an updating and merging-based similarity network fusion method, termed UM-SF, is presented to fuse various similarity networks with diverse weights. Finally, an intermediate layer-mediated multi-view feature projection representation method, termed IM-FP, is proposed to calculate the predicted DDA scores. This method uses multiple association scores to construct multi-view drug features, then projects them into disease space through the intermediate layer, where an intermediate layer similarity constraint is designed to learn the projection matrices. Results of comparative experiments reveal the effectiveness of our innovations. Comparisons with other state-of-the-art models by the 10-fold cross-validation experiment indicate our model's advantage on AUROC and AUPR metrics. Moreover, our proposed model successfully predicted 107 novel high-ranked DDAs.

Navigating with chemometrics and machine learning in chemistry

Chemometrics and machine learning are artificial intelligence-based methods stirring a transformative change in chemistry. Organic synthesis, drug discovery and analytical techniques are incorporating machine learning techniques at an accelerated pace. However, machine-assisted chemistry faces challenges while solving critical problems in chemistry due to complex relationships in data sets. Even with increasing publishing volumes on machine learning, its application in areas of chemistry is not a straightforward endeavour. A particular concern in applying machine learning in chemistry is data availability and reproducibility. The present review article discusses the various chemometric methods, expert systems, and machine learning techniques developed for solving problems of organic synthesis and drug discovery with selected examples. Further, a concise discussion on chemometrics and ML deployed in analytical techniques such as, spectroscopy, microscopy and chromatography are presented. Finally, the review reflects the challenges, opportunities and future perspectives on machine learning and automation in chemistry. The review concludes by pondering on some tough questions on applying machine learning and their possibility of navigation in the different terrains of chemistry.