activity
20182022
most citedHelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer

62 citations · 71 across the 4 of their papers we have counts for

collaborators

6 papers

q-bio.BM202262 cited

HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer

Shanzhuo Zhang, Zhiyuan Yan, Yueyang Huang +8

Accurate ADMET (an abbreviation for "absorption, distribution, metabolism, excretion, and toxicity") predictions can efficiently screen out undesirable drug candidates in the early…

math.HO2021

On the Cantor set and the Cantor-Lebesgue functions

Lihang Liu, Wilfredo O. Urbina

The ternary Cantor set , constructed by George Cantor in 1883, is the best known example of a perfect nowhere-dense set in the real line. The present article we study…

physics.chem-ph20211 cited

LiteGEM: Lite Geometry Enhanced Molecular Representation Learning for Quantum Property Prediction

Shanzhuo Zhang, Lihang Liu, Sheng Gao +6

In this report, we (SuperHelix team) present our solution to KDD Cup 2021-PCQM4M-LSC, a large-scale quantum chemistry dataset on predicting HOMO-LUMO gap of molecules. Our solution…

cs.IR2019

MBCAL: Sample Efficient and Variance Reduced Reinforcement Learning for Recommender Systems

Fan Wang, Xiaomin Fang, Lihang Liu +2

In recommender systems such as news feed stream, it is essential to optimize the long-term utilities in the continuous user-system interaction processes. Previous works have proved…

cs.IR20198 cited

Sequential Evaluation and Generation Framework for Combinatorial Recommender System

Fan Wang, Xiaomin Fang, Lihang Liu +5

In the combinatorial recommender systems, multiple items are fed to the user at one time in the result page, where the correlations among the items have impact on the user behavior…

cs.CV2018

Unsupervised Deep Domain Adaptation for Pedestrian Detection

Lihang Liu, Weiyao Lin, Lisheng Wu +2

This paper addresses the problem of unsupervised domain adaptation on the task of pedestrian detection in crowded scenes. First, we utilize an iterative algorithm to iteratively se…