Search Results for author:
Found 23 papers, 12 papers with code
AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation
Additionally, in the absence of AAV9 capsid data, apart from one wild-type sequence, we used the same model to directly generate a number of viable sequences with up to 9 mutations.
Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation
Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets.
Generative AI for Controllable Protein Sequence Design: A Survey
To set the stage, we first outline the foundational tasks in protein sequence design in terms of the constraints involved and present key generative models and optimization algorithms.
From molecules to scaffolds to functional groups: building context-dependent molecular representation via multi-channel learning
Self-supervised learning (SSL) has emerged as a popular solution, utilizing large-scale, unannotated molecular data to learn a foundational representation of chemical space that might be advantageous for downstream tasks.
Delete: Deep Lead Optimization Enveloped in Protein Pocket through Unified Deleting Strategies and a Structure-aware Network
Drug discovery is a highly complicated process, and it is unfeasible to fully commit it to the recently developed molecular generation methods.
Highly accurate and efficient deep learning paradigm for full-atom protein loop modeling with KarmaLoop
Protein loop modeling is the most challenging yet highly non-trivial task in protein structure prediction.
MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation
However, limited attention is paid to hierarchical generative models, which can exploit the inherent hierarchical structure (with rich semantic information) of the molecular graphs and generate complex molecules of larger size that we shall demonstrate to be difficult for most existing models.
An Equivariant Generative Framework for Molecular Graph-Structure Co-Design
Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provide new insights into machine learning-based molecule representation and generation.
Sample-efficient Multi-objective Molecular Optimization with GFlowNets
Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as a black-box optimization problem over the discrete chemical space.
Protein-Ligand Complex Generator & Drug Screening via Tiered Tensor Transform
The generation of small molecule candidate (ligand) binding poses in its target protein pocket is important for computer-aided drug discovery.
An adaptive graph learning method for automated molecular interactions and properties predictions
In fact, the pursuit of high prediction performance on a limited number of datasets has crystallized their architectures and hyperparameters, making them lose advantage in repurposing to new data generated in drug discovery.
Ranked #1 on
Drug Discovery
on ToxCast (Toxicity Forecaster)
Retroformer: Pushing the Limits of Interpretable End-to-end Retrosynthesis Transformer
In this paper, we propose Retroformer, a novel Transformer-based architecture for retrosynthesis prediction without relying on any cheminformatics tools for molecule editing.
Fast Extraction of Word Embedding from Q-contexts
In this work, we show that with merely a small fraction of contexts (Q-contexts)which are typical in the whole corpus (and their mutual information with words), one can construct high-quality word embedding with negligible errors.
RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions
RetroPrime achieves the Top-1 accuracy of 64. 8% and 51. 4%, when the reaction type is known and unknown, respectively, in the USPTO-50 K dataset.
Ranked #13 on
Single-step retrosynthesis
on USPTO-50k
Modeling Protein Using Large-scale Pretrain Language Model
The emergence of deep learning models makes modeling data patterns in large quantities of data possible.
Neural Predictor based Quantum Architecture Search
For instance, a key component of VQAs is the design of task-dependent parameterized quantum circuits (PQCs) as in the case of designing a good neural architecture in deep learning.
Neural Architecture Search
Quantum Physics
TrimNet: learning molecular representation from triplet messages for biomedicine
These advantages have established TrimNet as a powerful and useful computational tool in solving the challenging problem of molecular representation learning.
Ranked #1 on
Drug Discovery
on MUV
Differentiable Quantum Architecture Search
Hereby, we propose a general framework of differentiable quantum architecture search (DQAS), which enables automated designs of quantum circuits in an end-to-end differentiable fashion.
Quantum Physics
A quantum extension of SVM-perf for training nonlinear SVMs in almost linear time
We propose a quantum algorithm for training nonlinear support vector machines (SVM) for feature space learning where classical input data is encoded in the amplitudes of quantum states.
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
We introduce a new molecular dataset, named Alchemy, for developing machine learning models useful in chemistry and material science.
Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization
The IGNN model is based on an elegant and fundamental idea in information theory as explained in the main text, and it could be easily generalized beyond the contexts of molecular graphs considered in this work.
Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks
Our work is based on an excellent idea that whitening the inputs of neural networks can achieve a fast convergence speed.
Quantum algorithms for feedforward neural networks
The running times of our algorithms can be quadratically faster in the size of the network than their standard classical counterparts since they depend linearly on the number of neurons in the network, as opposed to the number of connections between neurons as in the classical case.
