Academic Articles & Research Papers

Quantum Computing in Medicine

Quantum computing (QC) represents a paradigm shift in computational power, offering unique capabilities for addressing complex problems that are infeasible for classical computers. This review paper provides a detailed account of the curren...

A Survey on Neural Network Interpretability

Along with the great success of deep neural networks, there is also growing concern about their black-box nature. The interpretability issue affects people's trust on deep learning systems. It is also related to many ethical problems, e.g.,...

A renaissance of neural networks in drug discovery

INTRODUCTION: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to cho...

Self-Normalizing Neural Networks

Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNN...

Combinatorial Materials Science and Catalysis

After forever changing the drug discovery process in the pharmaceutical industry, combinatorial chemistry methodologies are increasingly being applied to the discovery and optimization of more efficient catalysts and materials (see picture)...

Quantum Computing for Healthcare: A Review

In recent years, the interdisciplinary field of quantum computing has rapidly developed and garnered substantial interest from both academia and industry due to its ability to process information in fundamentally different ways, leading to ...

Coalition of explainable artificial intelligence and quantum computing in precision medicine

This survey examines the convergence of Explainable Artificial Intelligence (XAI) and Quantum Computing (QC) toward precision medicine. We review developments from 2018 to 2025, summarizing quantum algorithms, quantum-machine-learning model...

P2′ Site Scanning of Snake Kunitz-Type Toxin Analogue BF9-N17H with Unique P1 Site Histidine Highlights Novel XIa Inhibitory Peptides Without Plasmin Inhibitory Activity

Kunitz-type peptides from venomous and blood-sucking animals represent an important natural molecular resource for anticoagulant development towards XIa, a new target for antithrombotic drug discovery. However, the plasmin-inhibitory activi...

Cold storage of human precision-cut lung slices in TiProtec preserves cellular composition and transcriptional responses and enables on-demand mechanistic studies

Abstract Background Human precision-cut lung slices (hPCLS) are a unique platform for functional, mechanistic, and drug discovery studies in the field of respiratory research. However, tissue availability, generation, and cultivation time r...

Artificial Intelligence in Healthcare: Advancing Innovation and Ethics to Foster Well-Being

Artificial intelligence (AI) is reshaping healthcare by enhancing diagnostic precision, treatment personalization, and overall patient care. By leveraging technologies such as machine learning, deep learning, natural language processing, an...

Overview of Artificial Neural Networks

The artificial neural network (ANN), or simply neural network, is a machine learning method evolved from the idea of simulating the human brain. The data explosion in modem drug discovery research requires sophisticated analysis methods to ...

DVNDTA: a dual virtual node based heterogeneous interaction model for accurate prediction of drug-target affinity

Abstract Efficient prediction of drug-target affinity (DTA) is crucial for accelerating drug discovery. Recently, deep learning approaches leveraging 3D complexes have shown strong potential for drug-target affinity prediction. However, mos...

A deep-reinforcement learning approach for optimizing homogeneous droplet routing in digital microfluidic biochips

Over the past two decades, digital microfluidic biochips have been in much demand for safety-critical and biomedical applications and increasingly important in point-of-care analysis, drug discovery, and immunoassays, among other areas. How...

ACtriplet: An improved deep learning model for activity cliffs prediction by integrating triplet loss and pre-training

Activity cliffs (ACs) are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target. ACs offer crucial insights that aid m...

The rise of deep learning in drug discovery

Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other...

Deep Learning in Drug Discovery

Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical ...

Applications of Deep Learning in Biomedicine

Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this ...

Integrating Multisource Target Evidence with Machine-Learning Potency Prediction for Alzheimer’s Disease Drug Discovery.

Machine learning in pain research

1. Introduction Pain and pain chronification are incompletely understood and unresolved medical problems that continue to have a high prevalence.14 It has been accepted that pain is a complex phenomenon.2,32,72 Contemporary methods of compu...

Applications of machine learning in drug discovery and development