Search Results for author:
Found 13 papers, 2 papers with code
Physics Inspired Approaches To Understanding Gaussian Processes
Prior beliefs about the latent function to shape inductive biases can be incorporated into a Gaussian Process (GP) via the kernel.
Evaluating the roughness of structure-property relationships using pretrained molecular representations
When the correlation between structure and property weakens, a dataset is described as "rough," but this characteristic is partly a function of the chosen representation.
A Principled Method for the Creation of Synthetic Multi-fidelity Data Sets
Multifidelity and multioutput optimisation algorithms are of active interest in many areas of computational design as they allow cheaper computational proxies to be used intelligently to aid experimental searches for high-performing species.
Roughness of molecular property landscapes and its impact on modellability
In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space.
Self-focusing virtual screening with active design space pruning
In this study, we propose an extension to the framework of model-guided optimization that mitigates inferences costs using a technique we refer to as design space pruning (DSP), which irreversibly removes poor-performing candidates from consideration.
Using Bayesian Optimization to Accelerate Virtual Screening for the Discovery of Therapeutics Appropriate for Repurposing for COVID-19
The novel Wuhan coronavirus known as SARS-CoV-2 has brought almost unprecedented effects for a non-wartime setting, hitting social, economic and health systems hard.~ Being able to bring to bear pharmaceutical interventions to counteract its effects will represent a major turning point in the fight to turn the tides in this ongoing battle.~ Recently, the World's most powerful supercomputer, SUMMIT, was used to identify existing small molecule pharmaceuticals which may have the desired activity against SARS-CoV-2 through a high throughput virtual screening approach.
Privacy-Preserving Gaussian Process Regression -- A Modular Approach to the Application of Homomorphic Encryption
Much of machine learning relies on the use of large amounts of data to train models to make predictions.
Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning
Reinforcement Learning (RL) has demonstrated state-of-the-art results in a number of autonomous system applications, however many of the underlying algorithms rely on black-box predictions.
Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks
Chemical representations derived from deep learning are emerging as a powerful tool in areas such as drug discovery and materials innovation.
Dynamic Control of Explore/Exploit Trade-Off In Bayesian Optimization
Bayesian optimization offers the possibility of optimizing black-box operations not accessible through traditional techniques.
Efficient and Scalable Batch Bayesian Optimization Using K-Means
We present K-Means Batch Bayesian Optimization (KMBBO), a novel batch sampling algorithm for Bayesian Optimization (BO).
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space
These results show that PDTS is a successful solution for large-scale parallel BO.
Space-Filling Curves as a Novel Crystal Structure Representation for Machine Learning Models
While the structure representations based on atom connectivities are prevalent for molecules, two-dimensional descriptors are not suitable for describing molecular crystals.
