13 papers · ranked by Valyu relevance
Rohan Chandraghatgi, Hai-Feng Ji, Gail L. Rosen, Bahrad A. Sokhansanj
Recent advances in computational methods provide the promise of dramatically accelerating drug discovery. While math-ematical modeling and machine learning have become vital in predicting drug-target interactions and properties, there is untapped potential in computational drug discovery due to the vast and complex…
Weitang Li, Zhi Yin, Xiaoran Li, Dongqiang Ma + 8 more
Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which…
Woo-Young Ahn, Nathaniel Haines, Lei Zhang
Reinforcement learning and decision-making (RLDM) provide a quantitative framework, which allows us to specify psychiatric conditions with basic dimensions of neurocognitive functioning. RLDM offer a novel approach to assess and potentially diagnose psychiatric patients, and there is growing enthusiasm on RLDM and…
Alexander J. Bryer, Juan R. Perilla
Dimensionality reduction via coarse grain modeling has positioned itself as an indispensable tool for decades, particularly for biomolecular simulations where atomic systems encompass hundreds of millions of atoms. While distinct flavors of coarse grain modeling exist, those occupying the coarse end of the spectrum are…
Milena Rmus, Ti-Fen Pan, Liyu Xia, Anne G. E. Collins
Computational cognitive models have been used extensively to formalize cognitive processes. Model parameters offer a simple way to quantify individual differences in how humans process information. Similarly, model comparison allows researchers to identify which theories, embedded in different models, provide the best…
Jean-Louis Palgen, Angélique Perrillat-Mercerot, Nicoletta Ceres, Emmanuel Peyronnet + 6 more
Mechanistic models are built using knowledge as the primary information source, with well-established biological and physical laws determining the causal relationships within the model. Once the causal structure of the model is determined, parameters must be defined in order to accurately reproduce relevant data.…
Chris Rackauckas, Yingbo Ma, Andreas Noack, Vaibhav Dixit + 10 more
Pharmacometric modeling establishes causal quantitative relationships between administered dose, tissue exposures, desired and undesired effects and patient’s risk factors. These models are employed to de-risk drug development and guide precision medicine decisions. However, pharmacometric tools have not been designed…
David S. Cerutti, Rafal Wiewiora, Simon Boothroyd, Woody Sherman
The Structure and TOpology Replica Molecular Mechanics (STORMM) code is a next-generation molecular simulation engine and associated libraries optimized for performance on fast, multicore central processor units (CPUs) and graphics processing units (GPUs) with independent memory and tens of thousands of threads. STORMM…
Juan P. Franco, Karlo Doroc, Nitin Yadav, Peter Bossaerts + 1 more
The survival of human organisms depends on our ability to solve complex tasks in the face of limited cognitive resources. However, little is known about the factors that drive the complexity of those tasks. Here, building on insights from computational complexity theory, we quantify the computational hardness of…
Gustavo Deco, Yonatan Sanz Perl, Jakub Vohryzek, Andrea Luppi + 1 more
The perhaps most important unsolved problem in neuroscience is how the brain survives in a complex world by performing a rich repertoire of computation on a minimal energy budget. The brain is much better at adapting to the multiplicity of stimuli and outcomes than current generations of computers, artificial neural…
Erickson Fajiculay, Chao-Ping Hsu
Modeling biochemical systems can provide insights into behaviors that are difficult to observe or understand. It requires software, programming, and understanding of the system to build a model and study it. Softwares exist for systems biology modeling, but most support only certain types of modeling tasks. Desirable…
Ashar J. Malik, Chandra S. Verma
Quantum computers have demonstrated advantage in tackling problems considered hard for classical computers and hold promise for tackling complex problems in molecular mechanics such as mapping the conformational landscapes of biomolecules. This work attempts to explore a few ways in which classical data, relating to…
Ben Lambert, David J. Gavaghan, Simon Tavener
Biological systems have evolved a degree of robustness with respect to perturbations in their environment and this capability is essential for their survival. In applications ranging from therapeutics to conservation, it is important to understand not only the sensitivity of biological systems to changes in their…