no code implementations • 2 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.
no code implementations • 30 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.
no code implementations • 26 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.
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.
no code implementations • 1 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.
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.
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.
Ranked #5 on Multi-step retrosynthesis on USPTO-190
1 code implementation • NeurIPS 2019 • Hanjun Dai, Chengtao Li, Connor W. Coley, Bo Dai, Le Song
Retrosynthesis is one of the fundamental problems in organic chemistry.
Ranked #11 on Single-step retrosynthesis on USPTO-50k
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.
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.
Ranked #14 on Node Classification on PPI
no code implementations • 27 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.
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.
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.
1 code implementation • ICML 2017 • Zi Wang, Chengtao Li, Stefanie Jegelka, Pushmeet Kohli
Optimization of high-dimensional black-box functions is an extremely challenging problem.
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.
no code implementations • 13 Jul 2016 • Chengtao Li, Stefanie Jegelka, Suvrit Sra
In this note we consider sampling from (non-homogeneous) strongly Rayleigh probability measures.
no code implementations • 19 Mar 2016 • Chengtao Li, Stefanie Jegelka, Suvrit Sra
Its theoretical guarantees and empirical performance rely critically on the quality of the landmarks selected.
no code implementations • 7 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.
no code implementations • 4 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.