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Found 47 papers, 34 papers with code
OpenChemIE: An Information Extraction Toolkit For Chemistry Literature
Our machine learning models attain state-of-the-art performance when evaluated individually, and we meticulously annotate a challenging dataset of reaction schemes with R-groups to evaluate our pipeline as a whole, achieving an F1 score of 69. 5%.
Beyond Major Product Prediction: Reproducing Reaction Mechanisms with Machine Learning Models Trained on a Large-Scale Mechanistic Dataset
Mechanistic understanding of organic reactions can facilitate reaction development, impurity prediction, and in principle, reaction discovery.
Substrate Scope Contrastive Learning: Repurposing Human Bias to Learn Atomic Representations
In this work, we introduce a novel pre-training strategy, substrate scope contrastive learning, which learns atomic representations tailored to chemical reactivity.
Predictive Chemistry Augmented with Text Retrieval
Conventionally, chemoinformatics models are trained with extensive structured data manually extracted from the literature.
An algorithmic framework for synthetic cost-aware decision making in molecular design
Small molecules exhibiting desirable property profiles are often discovered through an iterative process of designing, synthesizing, and testing sets of molecules.
Pareto Optimization to Accelerate Multi-Objective Virtual Screening
The discovery of therapeutic molecules is fundamentally a multi-objective optimization problem.
Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks
Molecular Representation Learning (MRL) has proven impactful in numerous biochemical applications such as drug discovery and enzyme design.
MIST-CF: Chemical formula inference from tandem mass spectra
Importantly, MIST-CF learns in a data dependent fashion using a Formula Transformer neural network architecture and circumvents the need for fragmentation tree construction.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing
Reaction diagram parsing is the task of extracting reaction schemes from a diagram in the chemistry literature.
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.
Generating Molecular Fragmentation Graphs with Autoregressive Neural Networks
The accurate prediction of tandem mass spectra from molecular structures has the potential to unlock new metabolomic discoveries by augmenting the community's libraries of experimental reference standards.
Prefix-Tree Decoding for Predicting Mass Spectra from Molecules
Computational predictions of mass spectra from molecules have enabled the discovery of clinically relevant metabolites.
Reinforced Genetic Algorithm for Structure-based Drug Design
The neural models take the 3D structure of the targets and ligands as inputs and are pre-trained using native complex structures to utilize the knowledge of the shared binding physics from different targets and then fine-tuned during optimization.
De novo PROTAC design using graph-based deep generative models
After fine-tuning, predicted activity against a challenging POI increases from 50% to >80% with near-perfect chemical validity for sampled compounds, suggesting this is a promising approach for the optimization of large, PROTAC-like molecules for targeted protein degradation.
Computer-Aided Multi-Objective Optimization in Small Molecule Discovery
Molecular discovery is a multi-objective optimization problem that requires identifying a molecule or set of molecules that balance multiple, often competing, properties.
Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design
Shape-based virtual screening is widely employed in ligand-based drug design to search chemical libraries for molecules with similar 3D shapes yet novel 2D chemical structures compared to known ligands.
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.
MolScribe: Robust Molecular Structure Recognition with Image-To-Graph Generation
Molecular structure recognition is the task of translating a molecular image into its graph structure.
A graph representation of molecular ensembles for polymer property prediction
While doing so, we built a dataset of simulated electron affinity and ionization potential values for >40k polymers with varying monomer composition, stoichiometry, and chain architecture, which may be used in the development of other tailored machine learning approaches.
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.
pyscreener: A Python Wrapper for Computational Docking Software
pyscreener is a Python library that seeks to alleviate the challenges of large-scale structure-based design using computational docking.
Bringing Atomistic Deep Learning to Prime Time
Artificial intelligence has not yet revolutionized the design of materials and molecules.
Scalable Geometric Deep Learning on Molecular Graphs
Deep learning in molecular and materials sciences is limited by the lack of integration between applied science, artificial intelligence, and high-performance computing.
Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction
Synthesis planning and reaction outcome prediction are two fundamental problems in computer-aided organic chemistry for which a variety of data-driven approaches have emerged.
Ranked #10 on
Single-step retrosynthesis
on USPTO-50k
Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design
Molecular design and synthesis planning are two critical steps in the process of molecular discovery that we propose to formulate as a single shared task of conditional synthetic pathway generation.
Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations
Molecular chirality, a form of stereochemistry most often describing relative spatial arrangements of bonded neighbors around tetrahedral carbon centers, influences the set of 3D conformers accessible to the molecule without changing its 2D graph connectivity.
Differentiable Scaffolding Tree for Molecule Optimization
The structural design of functional molecules, also called molecular optimization, is an essential chemical science and engineering task with important applications, such as drug discovery.
Differentiable Scaffolding Tree for Molecular Optimization
The structural design of functional molecules, also called molecular optimization, is an essential chemical science and engineering task with important applications, such as drug discovery.
Machine learning modeling of family wide enzyme-substrate specificity screens
However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their natural chemical transformation on non-natural substrates.
Machine learning on DNA-encoded library count data using an uncertainty-aware probabilistic loss function
Further, this approach to uncertainty-aware regression is applicable to other sparse or noisy datasets where the nature of stochasticity is known or can be modeled; in particular, the Poisson enrichment ratio metric we use can apply to other settings that compare sequencing count data between two experimental conditions.
GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles
Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery.
Learning Graph Models for Template-Free Retrosynthesis
Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule.
Ranked #6 on
Single-step retrosynthesis
on USPTO-50k
BioNavi-NP: Biosynthesis Navigator for Natural Products
Nature, a synthetic master, creates more than 300, 000 natural products (NPs) which are the major constituents of FDA-proved drugs owing to the vast chemical space of NPs.
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
Here, we introduce Therapeutics Data Commons (TDC), the first unifying platform to systematically access and evaluate machine learning across the entire range of therapeutics.
Accelerating high-throughput virtual screening through molecular pool-based active learning
Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands.
Message Passing Networks for Molecules with Tetrahedral Chirality
Molecules with identical graph connectivity can exhibit different physical and biological properties if they exhibit stereochemistry-a spatial structural characteristic.
Learning Graph Models for Retrosynthesis Prediction
Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule.
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
While we believe these results show that existing UQ methods are not sufficient for all common use-cases and demonstrate the benefits of further research, we conclude with a practical recommendation as to which existing techniques seem to perform well relative to others.
Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in deep generative models.
Autonomous discovery in the chemical sciences part I: Progress
This two-part review examines how automation has contributed to different aspects of discovery in the chemical sciences.
Autonomous discovery in the chemical sciences part II: Outlook
This two-part review examines how automation has contributed to different aspects of discovery in the chemical sciences.
The Synthesizability of Molecules Proposed by Generative Models
The discovery of functional molecules is an expensive and time-consuming process, exemplified by the rising costs of small molecule therapeutic discovery.
Retrosynthesis Prediction with Conditional Graph Logic Network
Retrosynthesis is one of the fundamental problems in organic chemistry.
Ranked #11 on
Single-step retrosynthesis
on USPTO-50k
Learning retrosynthetic planning through self-play
The problem of retrosynthetic planning can be framed as one player game, in which the chemist (or a computer program) works backwards from a molecular target to simpler starting materials though a series of choices regarding which reactions to perform.
A graph-convolutional neural network model for the prediction of chemical reactivity
We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s).
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
The prediction of organic reaction outcomes is a fundamental problem in computational chemistry.
