Search Results for author:
Found 17 papers, 10 papers with code
UniGenX: Unified Generation of Sequence and Structure with Autoregressive Diffusion
We validate the effectiveness of UniGenX on material and small molecule generation tasks, achieving a significant leap in state-of-the-art performance for material crystal structure prediction and establishing new state-of-the-art results for small molecule structure prediction, de novo design, and conditional generation.
NatureLM: Deciphering the Language of Nature for Scientific Discovery
Foundation models have revolutionized natural language processing and artificial intelligence, significantly enhancing how machines comprehend and generate human languages.
Chimera: Accurate retrosynthesis prediction by ensembling models with diverse inductive biases
Planning and conducting chemical syntheses remains a major bottleneck in the discovery of functional small molecules, and prevents fully leveraging generative AI for molecular inverse design.
RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets
Single-step retrosynthesis aims to predict a set of reactions that lead to the creation of a target molecule, which is a crucial task in molecular discovery.
Generative Active Learning for the Search of Small-molecule Protein Binders
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge.
SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints
To address this, we introduce various strategies for learning the GFlowNet backward policy and thus demonstrate how additional constraints can be integrated into the GFlowNet MDP framework.
Re-evaluating Retrosynthesis Algorithms with Syntheseus
Automated Synthesis Planning has recently re-emerged as a research area at the intersection of chemistry and machine learning.
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.
RetroBridge: Modeling Retrosynthesis with Markov Bridges
Retrosynthesis planning is a fundamental challenge in chemistry which aims at designing reaction pathways from commercially available starting materials to a target molecule.
Are VAEs Bad at Reconstructing Molecular Graphs?
Many contemporary generative models of molecules are variational auto-encoders of molecular graphs.
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
Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks
Finding synthesis routes for molecules of interest is essential in the discovery of new drugs and materials.
Ranked #28 on
Single-step retrosynthesis
on USPTO-50k
Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction
Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials.
Learning to Extend Molecular Scaffolds with Structural Motifs
Recent advancements in deep learning-based modeling of molecules promise to accelerate in silico drug discovery.
Molecular representation learning with language models and domain-relevant auxiliary tasks
We apply a Transformer architecture, specifically BERT, to learn flexible and high quality molecular representations for drug discovery problems.
Exploring Deep Recurrent Models with Reinforcement Learning for Molecule Design
The design of small molecules with bespoke properties is of central importance to drug discovery.
Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies
Retrosynthesis is a technique to plan the chemical synthesis of organic molecules, for example drugs, agro- and fine chemicals.
