Search Results for author: Chengtao Li

Found 20 papers, 6 papers with code

AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design

no code implementations2 Apr 2024 Xinze Li, Penglei Wang, Tianfan Fu, Wenhao Gao, Chengtao Li, Leilei Shi, Junhong Liu

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.

Drug Discovery valid

Molecular Attributes Transfer from Non-Parallel Data

no code implementations30 Nov 2021 Shuangjia Zheng, Ying Song, Zhang Pan, Chengtao Li, Le Song, Yuedong Yang

Optimizing chemical molecules for desired properties lies at the core of drug development.

Attribute Style Transfer

BioNavi-NP: Biosynthesis Navigator for Natural Products

no code implementations26 May 2021 Shuangjia Zheng, Tao Zeng, Chengtao Li, Binghong Chen, Connor W. Coley, Yuedong Yang, Ruibo Wu

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.


Molecule Optimization by Explainable Evolution

no code implementations ICLR 2021 Binghong Chen, Tianzhe Wang, Chengtao Li, Hanjun Dai, Le Song

Optimizing molecules for desired properties is a fundamental yet challenging task in chemistry, material science and drug discovery.

Drug Discovery

PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation

no code implementations1 Jan 2021 Binghong Chen, Chengtao Li, Hanjun Dai, Rampi Ramprasad, Le Song

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.

Domain Adaptation Retrosynthesis

Group Contextual Encoding for 3D Point Clouds

1 code implementation NeurIPS 2020 Xu Liu, Chengtao Li, Jian Wang, Jingbo Wang, Boxin Shi, Xiaodong He

In this work, we extended the contextual encoding layer that was originally designed for 2D tasks to 3D Point Cloud scenarios.

Scene Understanding

Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search

1 code implementation ICML 2020 Binghong Chen, Chengtao Li, Hanjun Dai, Le Song

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.

Multi-step retrosynthesis

Improving Sequential Determinantal Point Processes for Supervised Video Summarization

no code implementations ECCV 2018 Aidean Sharghi, Ali Borji, Chengtao Li, Tianbao Yang, Boqing Gong

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.

Point Processes Supervised Video Summarization

Representation Learning on Graphs with Jumping Knowledge Networks

4 code implementations ICML 2018 Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, Stefanie Jegelka

Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance.

Graph Attention Node Classification +2

Robust GANs against Dishonest Adversaries

no code implementations27 Feb 2018 Zhi Xu, Chengtao Li, Stefanie Jegelka

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

1 code implementation ICLR 2018 Chengtao Li, David Alvarez-Melis, Keyulu Xu, Stefanie Jegelka, Suvrit Sra

We propose a framework for adversarial training that relies on a sample rather than a single sample point as the fundamental unit of discrimination.

Domain Adaptation

Polynomial Time Algorithms for Dual Volume Sampling

no code implementations NeurIPS 2017 Chengtao Li, Stefanie Jegelka, Suvrit Sra

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.

Experimental Design

Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling

no code implementations NeurIPS 2016 Chengtao Li, Stefanie Jegelka, Suvrit Sra

We consider the task of rapidly sampling from such constrained measures, and develop fast Markov chain samplers for them.

Point Processes

Fast DPP Sampling for Nyström with Application to Kernel Methods

no code implementations19 Mar 2016 Chengtao Li, Stefanie Jegelka, Suvrit Sra

Its theoretical guarantees and empirical performance rely critically on the quality of the landmarks selected.

Point Processes regression

Gauss quadrature for matrix inverse forms with applications

no code implementations7 Dec 2015 Chengtao Li, Suvrit Sra, Stefanie Jegelka

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.

BIG-bench Machine Learning Point Processes

Efficient Sampling for k-Determinantal Point Processes

no code implementations4 Sep 2015 Chengtao Li, Stefanie Jegelka, Suvrit Sra

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.

Point Processes

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