Search Results for author:
Found 9 papers, 8 papers with code
Stochastic Gradient Descent for Gaussian Processes Done Right
We study the optimisation problem associated with Gaussian process regression using squared loss.
Re-evaluating Retrosynthesis Algorithms with Syntheseus
The planning of how to synthesize molecules, also known as retrosynthesis, has been a growing focus of the machine learning and chemistry communities in recent years.
Retro-fallback: retrosynthetic planning in an uncertain world
Retrosynthesis is the task of planning a series of chemical reactions to create a desired molecule from simpler, buyable molecules.
Genetic algorithms are strong baselines for molecule generation
Generating molecules, both in a directed and undirected fashion, is a huge part of the drug discovery pipeline.
Retrosynthetic Planning with Dual Value Networks
Retrosynthesis, which aims to find a route to synthesize a target molecule from commercially available starting materials, is a critical task in drug discovery and materials design.
Ranked #1 on
Multi-step retrosynthesis
on USPTO-190
GAUCHE: A Library for Gaussian Processes in Chemistry
By defining such kernels in GAUCHE, we seek to open the door to powerful tools for uncertainty quantification and Bayesian optimisation in chemistry.
Meta-learning Adaptive Deep Kernel Gaussian Processes for Molecular Property Prediction
We propose Adaptive Deep Kernel Fitting with Implicit Function Theorem (ADKF-IFT), a novel framework for learning deep kernel Gaussian processes (GPs) by interpolating between meta-learning and conventional deep kernel learning.
A Fresh Look at De Novo Molecular Design Benchmarks
De novo molecular design is a thriving research area in machine learning (ML) that lacks ubiquitous, high-quality, standardized benchmark tasks.
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
We introduce an improved method for efficient black-box optimization, which performs the optimization in the low-dimensional, continuous latent manifold learned by a deep generative model.
Ranked #1 on
Molecular Graph Generation
on ZINC
