Dear Colleagues,

During the last decades, the increased rate of data production across all the fields of Life Sciences has profoundly changed our perception of data analysis and exploitation, which have become essential instruments for effective decision-making in several scientific disciplines. From the computational medicinal chemistry standpoint, we are at the verge of witnessing a paradigm shift triggered by the so-called big data era in the context of drug discovery and development. Accordingly, extracting relevant information from the ever-increasing volume of data and learning from it, is becoming of primary importance for achieving any substantial progress in the field. Two are the aspects for which addressing this challenge is particularly urgent. First, owing to the decline of the conventional “one target–one drug” paradigm of drug discovery, a growing awareness of the need to deal with the intrinsic complexity of several diseases has shifted the focus towards a more holistic perspective peculiar to systems biology approaches. In this context, modern drug design is taking advantage of big data analysis and related techniques by integrating an unprecedented amount of heterogeneous data coming from genomics, biology, and clinical sources. This is especially relevant for the emerging fields of polypharmacology, precision medicine, and drug repurposing. Second, thanks to the advancement of computational power, molecular dynamics (MD) simulations have recently emerged as a mature technique to assist drug discovery and development by complementing and expanding the scope of more conventional molecular docking tools which are limited to a static description of drug–target interactions. One of the challenges to be faced by MD simulations in medicinal chemistry is the widening gap between the speed of computations and the bottleneck currently represented by trajectory analysis. In this respect, the forefront of research in this area is the development of automated and efficient analysis tools for extracting robust mechanistic interpretations of the simulated events and accurately estimating relevant observables.

In this Special Issue, experts are invited to present original and review articles that contribute to the advancement of big data analysis in computational medicinal chemistry and, more generally, in drug discovery and development. A special emphasis is placed on the development and/or application of machine learning approaches and related techniques to pharmaceutically relevant problems. The range of applications spans from network-based analysis for integrating heterogeneous data to the use of advanced dimensionality reduction techniques for the extraction of reaction coordinates and/or the derivation of kinetic models from MD trajectories.

Dr. Matteo Masetti
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceuticals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• Neural Networks
• Network Analysis
• Dimensionality Reduction
• Reaction Coordinates
• Collective Variables
• Markov State Models

• Molecular Dynamics in Drug Design in Pharmaceuticals (3 articles)
• Chemoinformatics and Drug Design in Pharmaceuticals (7 articles)