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Found 20 papers, 6 papers with code
AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design
Structure-based drug design (SBDD), which aims to generate molecules that can bind tightly to the target protein, is an essential problem in drug discovery, and previous approaches have achieved initial success.
Molecular Attributes Transfer from Non-Parallel Data
Optimizing chemical molecules for desired properties lies at the core of drug development.
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.
PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation
We demonstrate that our method is able to propose high-quality polymerization plans for a dataset of 52 real-world polymers, of which a significant portion successfully recovers the currently-in-used polymerization processes in the real world.
Molecule Optimization by Explainable Evolution
Optimizing molecules for desired properties is a fundamental yet challenging task in chemistry, material science and drug discovery.
Group Contextual Encoding for 3D Point Clouds
In this work, we extended the contextual encoding layer that was originally designed for 2D tasks to 3D Point Cloud scenarios.
Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search
Retrosynthetic planning is a critical task in organic chemistry which identifies a series of reactions that can lead to the synthesis of a target product.
Ranked #5 on
Multi-step retrosynthesis
on USPTO-190
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
Improving Sequential Determinantal Point Processes for Supervised Video Summarization
In terms of modeling, we design a new probabilistic distribution such that, when it is integrated into SeqDPP, the resulting model accepts user input about the expected length of the summary.
Representation Learning on Graphs with Jumping Knowledge Networks
Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance.
Ranked #14 on
Node Classification
on PPI
Robust GANs against Dishonest Adversaries
We explore a notion of robustness for generative adversarial models that is pertinent to their internal interactive structure, and show that, perhaps surprisingly, the GAN in its original form is not robust.
Distributional Adversarial Networks
We propose a framework for adversarial training that relies on a sample rather than a single sample point as the fundamental unit of discrimination.
Polynomial Time Algorithms for Dual Volume Sampling
We study dual volume sampling, a method for selecting k columns from an n x m short and wide matrix (n <= k <= m) such that the probability of selection is proportional to the volume spanned by the rows of the induced submatrix.
Batched High-dimensional Bayesian Optimization via Structural Kernel Learning
Optimization of high-dimensional black-box functions is an extremely challenging problem.
Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling
We consider the task of rapidly sampling from such constrained measures, and develop fast Markov chain samplers for them.
Fast Sampling for Strongly Rayleigh Measures with Application to Determinantal Point Processes
In this note we consider sampling from (non-homogeneous) strongly Rayleigh probability measures.
Fast DPP Sampling for Nyström with Application to Kernel Methods
Its theoretical guarantees and empirical performance rely critically on the quality of the landmarks selected.
Gauss quadrature for matrix inverse forms with applications
We present a framework for accelerating a spectrum of machine learning algorithms that require computation of bilinear inverse forms $u^\top A^{-1}u$, where $A$ is a positive definite matrix and $u$ a given vector.
Efficient Sampling for k-Determinantal Point Processes
Our method takes advantage of the diversity property of subsets sampled from a DPP, and proceeds in two stages: first it constructs coresets for the ground set of items; thereafter, it efficiently samples subsets based on the constructed coresets.
