Improving Compound–Protein Interaction Prediction by Self-Training with Augmenting Negative Samples

Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Since experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI databases have accelerated the development of many machine-learning methods for CPI predictions. However, their performance, particularly their generalizability against external data, often suffers from a data imbalance attributed to the lack of experimentally validated inactive (negative) samples. In this study, we developed a self-training method for augmenting both credible and informative negative samples to improve the performance of models impaired by data imbalances. The constructed model demonstrated higher performance than those constructed with other conventional methods for solving data imbalances, and the improvement was prominent for external datasets. Moreover, examination of the prediction score thresholds for pseudo-labeling during self-training revealed that augmenting the samples with ambiguous prediction scores is beneficial for constructing a model with high generalizability. The present study provides guidelines for improving CPI predictions on real-world data, thus facilitating drug discovery

Improving Compound-Protein Interaction Prediction by Self-Training with Augmenting Negative Samples

Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Because experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI databases have accelerated the development of many machine learning methods for CPI predictions. However, their performance, particularly their generalizability against external data, often suffers from a data imbalance attributed to the lack of experimentally validated inactive (negative) samples. In this study, we developed a self-training method for augmenting both credible and informative negative samples to improve the performance of models impaired by data imbalances. The constructed model demonstrated a higher performance than those constructed with other conventional methods for solving data imbalances, and the improvement was prominent for external datasets. Moreover, examination of the prediction score thresholds for pseudo-labeling during self-training revealed that augmenting the samples with ambiguous prediction scores is beneficial for constructing a model with high generalizability. The present study provides guidelines for improving CPI predictions on real-world data, thus facilitating drug discovery

VGAE-MCTS: a New Molecular Generative Model combining Variational Graph Auto-Encoder and Monte Carlo Tree Search

Molecular generation is crucial for advancing drug discovery, material design, and chemical exploration. It expedites the search for new drug candidates, facilitates tailored material creation, and enhances our understanding of molecular diversity. By employing artificial intelligence techniques, such as molecular generative models based on molecular graphs, researchers have tackled the challenge of efficiently molecules with desired properties. We proposed a new molecular generative model combining deep learning and reinforcement learning evaluated the validity, novelty, and optimized physicochemical properties of the generated molecules. Importantly, the model explored uncharted regions of chemical space, allowing for the efficient discovery and design of new molecules. This innovative approach has significant potential to revolutionize drug discovery, material science, and chemical research for accelerating scientific innovation. By leveraging advanced techniques and exploring previously unexplored chemical spaces, this study offers promising prospects for the efficient discovery and design of new molecules in the field of drug development

Elevated α-defensin levels in plasma and bronchoalveolar lavage fluid from patients with myositis-associated interstitial lung disease

Background: Interstitial lung disease (ILD) is a prognostic indicator of poor outcome in myositis. Although the pathogenesis of myositis-associated ILD is not well understood, neutrophils are thought to play a pivotal role. Neutrophils store azurophil granules that contain defensins, which are antimicrobial peptides that regulate the inflammatory response. Here, we evaluated levels of the human neutrophil peptides (HNPs) α-defensin 1 through 3 in patients with myositis-associated ILD to determine whether HNPs represent disease markers and play a role in the pathogenesis of myositis-associated ILD. Methods: HNP levels were measured in the plasma and bronchoalveolar lavage fluid (BALF) of 56 patients with myositis-associated ILD and 24 healthy controls by enzyme-linked immunosorbent assay. Results: Analysis revealed significantly higher HNP levels in plasma and BALF samples from patients with myositis-associated ILD as compared to those of healthy controls; however, plasma HNPs were significantly correlated with total cell counts in BALF. Additionally, BALF HNP levels were positively correlated with serum surfactant protein-A and the percentage of neutrophils in BALF, and BALF HNP levels correlated with the percentage of reticular opacities in high-resolution computed tomography results for patients with anti-aminoacyl-tRNA synthetase (ARS) antibody positive myositis-associated ILD. Survival did not differ between patients with higher and lower levels of plasma and BALF HNPs. Conclusions: Plasma and BALF HNPs might reflect the disease activities of myositis-associated ILD, especially in patients with anti-ARS antibody positive myositis-associated ILD. However further studies are necessary to clarify whether HNPs represent disease markers and play roles in disease pathogenesis