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A Scientific Reasoning Model for Organic Synthesis Procedure Generation
Guoqing Liu, Junren Li, Zihan Zhao +7
Solving computer-aided synthesis planning is essential for enabling fully automated, robot-assisted synthesis workflows and improving the efficiency of drug discovery. A key challe…
UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials
Gongbo Zhang, Yanting Li, Renqian Luo +31
Function in natural systems arises from one-dimensional sequences forming three-dimensional structures with specific properties. However, current generative models suffer from crit…
Challenging reaction prediction models to generalize to novel chemistry
John Bradshaw, Anji Zhang, Babak Mahjour +3
Deep learning models for anticipating the products of organic reactions have found many use cases, including validating retrosynthetic pathways and constraining synthesis-based mol…
Chemist-aligned retrosynthesis by ensembling diverse inductive bias models
Krzysztof Maziarz, Guoqing Liu, Hubert Misztela +8
Chemical synthesis remains a critical bottleneck in the discovery and manufacture of functional small molecules. AI-based synthesis planning models could be a potential remedy to f…
RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets
Piotr Gaiński, Michał Koziarski, Krzysztof Maziarz +3
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. Although a target mole…
SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints
Miruna Cretu, Charles Harris, Ilia Igashov +6
Generative models see increasing use in computer-aided drug design. However, while performing well at capturing distributions of molecular motifs, they often produce synthetically…
Re-evaluating Retrosynthesis Algorithms with Syntheseus
Krzysztof Maziarz, Austin Tripp, Guoqing Liu +5
Automated Synthesis Planning has recently re-emerged as a research area at the intersection of chemistry and machine learning. Despite the appearance of steady progress, we argue t…
Are VAEs Bad at Reconstructing Molecular Graphs?
Hagen Muenkler, Hubert Misztela, Michal Pikusa +3
Many contemporary generative models of molecules are variational auto-encoders of molecular graphs. One term in their training loss pertains to reconstructing the input, yet recons…
Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction
Philipp Seidl, Philipp Renz, Natalia Dyubankova +6
Finding synthesis routes for molecules of interest is an essential step in the discovery of new drugs and materials. To find such routes, computer-assisted synthesis planning (CASP…
Barking up the right tree: an approach to search over molecule synthesis DAGs
John Bradshaw, Brooks Paige, Matt J. Kusner +2
When designing new molecules with particular properties, it is not only important what to make but crucially how to make it. These instructions form a synthesis directed acyclic gr…
Molecular representation learning with language models and domain-relevant auxiliary tasks
Benedek Fabian, Thomas Edlich, Héléna Gaspar +4
We apply a Transformer architecture, specifically BERT, to learn flexible and high quality molecular representations for drug discovery problems. We study the impact of using diffe…
RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design
Cheng-Hao Liu, Maksym Korablyov, Stanisław Jastrzębski +3
De novo molecule generation often results in chemically unfeasible molecules. A natural idea to mitigate this problem is to bias the search process towards more easily synthesizabl…
World Programs for Model-Based Learning and Planning in Compositional State and Action Spaces
Marwin H. S. Segler
Some of the most important tasks take place in environments which lack cheap and perfect simulators, thus hampering the application of model-free reinforcement learning (RL). While…
A Model to Search for Synthesizable Molecules
John Bradshaw, Brooks Paige, Matt J. Kusner +2
Deep generative models are able to suggest new organic molecules by generating strings, trees, and graphs representing their structure. While such models allow one to generate mole…
DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation
Rim Assouel, Mohamed Ahmed, Marwin H Segler +2
Generating novel molecules with optimal properties is a crucial step in many industries such as drug discovery. Recently, deep generative models have shown a promising way of perfo…