In the past two decades, machine learning potentials (MLPs) have reached a level of
maturity that now enables applications to large-scale atomistic simulations of a wide range …

Adaptation and innovation are extremely important to the manufacturing industry. This
development should lead to sustainable manufacturing using new technologies. To promote …

Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning
platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop …

The remarkable flexibility and adaptability of ensemble methods and deep learning models
have led to the proliferation of their application in bioinformatics research. Traditionally …

Abstract Machine learning has become a pivotal tool for many projects in computational
biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical …

The screening of novel materials with good performance and the modelling of quantitative
structure-activity relationships (QSARs), among other issues, are hot topics in the field of …

Structural and physiochemical descriptors extracted from sequence data have been widely
used to represent sequences and predict structural, functional, expression and interaction …

The identification of interactions between drugs/compounds and their targets is crucial for
the development of new drugs. In vitro screening experiments (ie bioassays) are frequently …

Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate
understanding of the sequence (-structure)-function paradigm for DNAs, RNAs and proteins …

Peptides are biopolymers, typically consisting of 2–50 amino acids. They are biologically
produced by the cellular ribosomal machinery or by non-ribosomal enzymes and …