Data sparseness is a major limiting factor for deep machine learning. In the natural sciences, data distributions are heterogeneous. For instance, in chemistry and early-phase drug discovery, compound and molecular property data are typically sparse ...
Journal of computer-aided molecular design
Oct 7, 2025
Drug discovery relies on the ability to predict drug-target affinity (DTA), which allows for the efficient identification of drug candidates for certain protein targets. Scalability, accuracy, and interpretability are issues that traditional methods ...
Machine learning (ML) is increasingly used in DNA-encoded library (DEL) screening for ligand discovery, but its success depends on access to suitable data sets, which are often proprietary and costly. To overcome this, we present the first fully open...
Journal of computer-aided molecular design
Oct 4, 2025
The traditional drug discovery process is often lengthy, costly, and characterized by a high failure rate. There is a pressing need for innovative strategies to optimize this process and improve the chances of identifying effective therapeutic candid...
Journal of chemical information and modeling
Oct 3, 2025
Accurately predicting protein-ligand binding affinity (PLA) is essential in drug discovery for identifying lead compounds. The sequence and structural contexts of an amino acid residue (i.e., microenvironment) describe the surrounding chemical proper...
Epilepsy is one of the most prevalent neurological disorders, affecting approximately 23 million people in Asia alone. It is a disorder with severe social impacts and is going to progressively damage the brain. It encompasses a wide range of syndrome...
The emergence of artificial intelligence in protein structure prediction has significantly advanced our understanding of protein folding. Yet, challenges remain in accurately modeling intrinsically disordered proteins (IDPs) and capturing conformatio...
Journal of computer-aided molecular design
Sep 26, 2025
Kinase profiling is an essential step in both hit identification and selectivity evaluation. Since in vitro testing of large chemical libraries is costly and time-consuming, a computational approach can be applied to narrow down the reasonable chemic...
BACKGROUND: Activity cliffs, which refer to pairs of molecules that are structurally similar but show significant differences in their potency, can lead to model representation collapse and make the model challenging to distinguish them.
Journal of chemical information and modeling
Sep 22, 2025
In this work, we introduce auxiliary discriminator sequence generative adversarial networks (ADSeqGAN), a novel approach for molecular generation in small-sample data sets. Traditional generative models often struggle with limited training data, part...
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