Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry

Innovative applications of artificial intelligence during the COVID-19 pandemic

The COVID-19 pandemic has created unprecedented challenges worldwide. Artificial intelligence (AI) technologies hold tremendous potential for tackling key aspects of pandemic management and response. In the present review, we discuss the tremendous possibilities of AI technology in addressing the global challenges posed by the COVID-19 pandemic. First, we outline the multiple impacts of the current pandemic on public health, the economy, and society. Next, we focus on the innovative applications of advanced AI technologies in key areas such as COVID-19 prediction, detection, control, and drug discovery for treatment. Specifically, AI-based predictive analytics models can use clinical, epidemiological, and omics data to forecast disease spread and patient outcomes. Additionally, deep neural networks enable rapid diagnosis through medical imaging. Intelligent systems can support risk assessment, decision-making, and social sensing, thereby improving epidemic control and public health policies. Furthermore, high-throughput virtual screening enables AI to accelerate the identification of therapeutic drug candidates and opportunities for drug repurposing. Finally, we discuss future research directions for AI technology in combating COVID-19, emphasizing the importance of interdisciplinary collaboration. Though promising, barriers related to model generalization, data quality, infrastructure readiness, and ethical risks must be addressed to fully translate these innovations into real-world impacts. Multidisciplinary collaboration engaging diverse expertise and stakeholders is imperative for developing robust, responsible, and human-centered AI solutions against COVID-19 and future public health emergencies.

Immunogenic cell death (ICD) enhancers-Drugs that enhance the perception of ICD by dendritic cells

The search for immunostimulatory drugs applicable to cancer immunotherapy may profit from target-agnostic methods in which agents are screened for their functional impact on immune cells cultured in vitro without any preconceived idea on their mode of action. We have built a synthetic mini-immune system in which stressed and dying cancer cells (derived from standardized cell lines) are confronted with dendritic cells (DCs, derived from immortalized precursors) and CD8+ T-cell hybridoma cells expressing a defined T-cell receptor. Using this system, we can identify three types of immunostimulatory drugs: (i) pharmacological agents that stimulate immunogenic cell death (ICD) of malignant cells; (ii) drugs that act on DCs to enhance their response to ICD; and (iii) drugs that act on T cells to increase their effector function. Here, we focus on strategies to develop drugs that enhance the perception of ICD by DCs and to which we refer as "ICD enhancers." We discuss examples of ICD enhancers, including ligands of pattern recognition receptors (exemplified by TLR3 ligands that correct the deficient function of DCs lacking FPR1) and immunometabolic modifiers (exemplified by hexokinase-2 inhibitors), as well as methods for target deconvolution applicable to the mechanistic characterization of ICD enhancers.

Recent Progress and Prospects of Small Molecules for NLRP3 Inflammasome Inhibition

NLRP3 inflammasome is a multiprotein complex involved in host immune response─which exerts various biological effects by mediating the maturation and secretion of IL-1β and IL-18─and pyroptosis. However, its aberrant activation could cause amplification of inflammatory effects, thereby triggering a range of ailments, including Alzheimer's disease, Parkinson's disease, rheumatoid arthritis, gout, type 2 diabetes mellitus, and cancer. For the past few years, as an attractive anti-inflammatory target, NLRP3-targeting small-molecule inhibitors have been widely reported by both the academic and the industrial communities. In order to deeply understand the advancement of NLRP3 inflammasome inhibitors, we provide comprehensive insights and commentary on drugs currently under clinical investigation, as well as other NLRP3 inflammasome inhibitors from a chemical structure point of view, with an aim to provide new insights for the further development of clinical drugs for NLRP3 inflammasome-mediated diseases.

Machine Learning Methods for Small Data Challenges in Molecular Science

Small data are often used in scientific and engineering research due to the presence of various constraints, such as time, cost, ethics, privacy, security, and technical limitations in data acquisition. However, big data have been the focus for the past decade, small data and their challenges have received little attention, even though they are technically more severe in machine learning (ML) and deep learning (DL) studies. Overall, the small data challenge is often compounded by issues, such as data diversity, imputation, noise, imbalance, and high-dimensionality. Fortunately, the current big data era is characterized by technological breakthroughs in ML, DL, and artificial intelligence (AI), which enable data-driven scientific discovery, and many advanced ML and DL technologies developed for big data have inadvertently provided solutions for small data problems. As a result, significant progress has been made in ML and DL for small data challenges in the past decade. In this review, we summarize and analyze several emerging potential solutions to small data challenges in molecular science, including chemical and biological sciences. We review both basic machine learning algorithms, such as linear regression, logistic regression (LR), k-nearest neighbor (KNN), support vector machine (SVM), kernel learning (KL), random forest (RF), and gradient boosting trees (GBT), and more advanced techniques, including artificial neural network (ANN), convolutional neural network (CNN), U-Net, graph neural network (GNN), Generative Adversarial Network (GAN), long short-term memory (LSTM), autoencoder, transformer, transfer learning, active learning, graph-based semi-supervised learning, combining deep learning with traditional machine learning, and physical model-based data augmentation. We also briefly discuss the latest advances in these methods. Finally, we conclude the survey with a discussion of promising trends in small data challenges in molecular science.

High-throughput screening strategies for space-based radiation countermeasure discovery

As humanity begins to venture further into space, approaches to better protect astronauts from the hazards found in space need to be developed. One particular hazard of concern is the complex radiation that is ever present in deep space. Currently, it is unlikely enough spacecraft shielding could be launched that would provide adequate protection to astronauts during long-duration missions such as a journey to Mars and back. In an effort to identify other means of protection, prophylactic radioprotective drugs have been proposed as a potential means to reduce the biological damage caused by this radiation. Unfortunately, few radioprotectors have been approved by the FDA for usage and for those that have been developed, they protect normal cells/tissues from acute, high levels of radiation exposure such as that from oncology radiation treatments. To date, essentially no radioprotectors have been developed that specifically counteract the effects of chronic low-dose rate space radiation. This review highlights how high-throughput screening (HTS) methodologies could be implemented to identify such a radioprotective agent. Several potential target, pathway, and phenotypic assays are discussed along with potential challenges towards screening for radioprotectors. Utilizing HTS strategies such as the ones proposed here have the potential to identify new chemical scaffolds that can be developed into efficacious radioprotectors that are specifically designed to protect astronauts during deep space journeys. The overarching goal of this review is to elicit broader interest in applying drug discovery techniques, specifically HTS towards the identification of radiation countermeasures designed to be efficacious towards the biological insults likely to be encountered by astronauts on long duration voyages.

Versatile tools of synthetic biology applied to drug discovery and production

Although synthetic biology is an emerging research field, which has come to prominence within the last decade, it already has many practical applications. Its applications cover the areas of pharmaceutical biotechnology and drug discovery, bringing essential novel methods and strategies such as metabolic engineering, reprogramming the cell fate, drug production in genetically modified organisms, molecular glues, functional nucleic acids and genome editing. This review discusses the main avenues for synthetic biology application in pharmaceutical biotechnology. The authors believe that synthetic biology will reshape drug development and drug production to a similar extent as the advances in organic chemical synthesis in the 20th century. Therefore, synthetic biology already plays an essential role in pharmaceutical, biotechnology, which is the main focus of this review.

De novo molecular design with deep molecular generative models for PPI inhibitors

We construct a protein-protein interaction (PPI) targeted drug-likeness dataset and propose a deep molecular generative framework to generate novel drug-likeness molecules from the features of the seed compounds. This framework gains inspiration from published molecular generative models, uses the key features associated with PPI inhibitors as input and develops deep molecular generative models for de novo molecular design of PPI inhibitors. For the first time, quantitative estimation index for compounds targeting PPI was applied to the evaluation of the molecular generation model for de novo design of PPI-targeted compounds. Our results estimated that the generated molecules had better PPI-targeted drug-likeness and drug-likeness. Additionally, our model also exhibits comparable performance to other several state-of-the-art molecule generation models. The generated molecules share chemical space with iPPI-DB inhibitors as demonstrated by chemical space analysis. The peptide characterization-oriented design of PPI inhibitors and the ligand-based design of PPI inhibitors are explored. Finally, we recommend that this framework will be an important step forward for the de novo design of PPI-targeted therapeutics.