In Silico Drug Design and Discovery: Big Data for Small Molecule Design

Dear Colleagues,

Life sciences heavily rely on data collected in different ways, for example, through experimental work, medical observations, or computer simulations, to name a few. Advances in novel technologies, such as high-throughput screening and readout, next-generation sequencing, and “-omics” approaches, represent the main drivers of the exponentially increasing amount of data being generated at a fast pace, part of which is available in public databases (e.g., ChEMBL, PubChem, PDB).

Taking advantage of this wealth of information is critical to improve decision making in drug discovery projects. For instance, structure–activity relationships (SARs) can be extracted on a large scale and used to complement chemical optimization efforts. 

Therefore, there is a growing interest in computational approaches to exploit this amount of data and their complexity, including data mining and visualization techniques, predictive models, and machine learning algorithms. 

In this context, this Special Issue has been conceptualized to showcase recent progresses and current trends in the use of in silico approaches leveraging big data and extracting useful knowledge to support all aspects of drug design and discovery. Topics of interest include but are not limited to data mining, molecular modeling, compound bioactivity prediction, and machine learning. Experimental and theoretical research studies are welcome; multidisciplinary approaches are particularly encouraged.

We look forward to your contributions.

Prof. Antonio Lavecchia
Dr. Carmen Cerchia
Guest Editors

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