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Autoencoders are versatile tools for molecular informatics with the opportunity for advancing molecule and drug design. In this Review, the authors highlight the active areas of development in the field and explore the challenges that need to be addressed moving forward.
The capability of predicting stable materials is important to further accelerate the discovery of novel materials. In this Review, the authors discuss recent developments in machine learning techniques for assessing the stability of materials and highlight the opportunities in further advancing the field.
Identifying the origins and nature of non-genetic heterogeneity in cancer has widespread clinical ramifications. In this Review, the authors discuss how computational models and tools have been used to provide insights into this phenomenon and how they can help tackle the disease in the future.
The computational characterization of short-range order in compositionally complex materials relies on effective interatomic potentials. In this Review, challenges and opportunities in developing advanced potentials for such systems are discussed, with a focus on machine learning-based potentials.
Immunotherapy has begun to make a transformative impact on oncology practice, and mathematical modeling has been used to provide quantitative insights into this field. This Review discusses how models are being designed for direct clinical integration to improve the success rate of immunotherapy.
Quantum defects in two-dimensional materials offer promises for the next-generation quantum information technology. However, the rational design of these defects faces challenges, and thus, requires the development of advanced theoretical and computational models.
Massive datasets have been made available to enable systematic studies of gene regulation and its control via epigenetic mechanisms. In this Review, state-of-the-art computational methods used to effectively extract knowledge from these datasets are presented and discussed.
Computational approaches for drug repurposing can accelerate the identification of treatments during a pandemic. In this Review, the authors discuss this topic in the context of COVID-19 and propose a strategy to make computational drug repurposing more effective in future pandemics.
