With the accessible data found in biosciences, it becomes essential to rely on artificial intelligence and machine learning to analyse considerable amounts of data, carry out data analysis tasks, and productively progress at a faster pace. Biosciences can be related to a few fields such as agriculture, medical science, chemistry, biomechanics, and industry. Opportunities are arising for new applications focusing on machine learning methods, artificial intelligence approaches, and big data analysis in biomaterials, bioenergies, biomedicines, biofuels, drugs, and proteins. In the meantime, the increasing accessibility of large datasets extracted from quantitative human motion analysis is progressively opening new research areas such as human gait, biomechanics, and motor control research.

Moreover, machine learning has a long successful history in the pharmaceutical sector, helping discover and optimise new drugs such as predicting useful physicochemical properties like aqueous solubility. Therefore, the applications of machine learning in medicine have grown greatly in the last decade. Machine learning approaches such as supervised, unsupervised, and reinforcement learning techniques are commonly used in the field of bioenergy, biomass, and biomedicine for prediction, classification, and other purposes. These techniques are often combined with data reduction procedures for feature extraction. Recently, multiple investigations have been focused on new experimental methods, and new data analysis models.

The aim of this Special Issue is to solicit original research articles, as well as review articles, highlighting experimental methods, and novel data analysis involving intelligence methods to answer chemical, clinical, and engineering questions. Submissions focusing on the application of intelligence methods in biosciences through experimental methods or data analysis approaches are particularly encouraged.

Potential topics include but are not limited to the following:

• Computational biochemistry
• Biomedicine
• Bioenergy
• Biofuels
• Biomechanics
• Biomass
• Drug discovery
• Artificial intelligence methods
• Machine learning
• Feature selections in biosciences
• Model order reduction
• Deep learning
• Prediction
• Data preparation