Search Results for author:
Found 9 papers, 5 papers with code
MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension
Large language models are playing an increasingly significant role in molecular research, yet existing models often generate erroneous information, posing challenges to accurate molecular comprehension.
InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery
The rapid evolution of artificial intelligence in drug discovery encounters challenges with generalization and extensive training, yet Large Language Models (LLMs) offer promise in reshaping interactions with complex molecular data.
Ranked #20 on
Molecule Captioning
on ChEBI-20
Robust Bayesian optimization with reinforcement learned acquisition functions
In Bayesian optimization (BO) for expensive black-box optimization tasks, acquisition function (AF) guides sequential sampling and plays a pivotal role for efficient convergence to better optima.
Multimodal Pre-Training Model for Sequence-based Prediction of Protein-Protein Interaction
However, neither sequences nor contact maps can fully characterize structures and functions of the proteins, which are closely related to the PPI problem.
Docking-based Virtual Screening with Multi-Task Learning
Machine learning shows great potential in virtual screening for drug discovery.
Dimension Reduction for Data with Heterogeneous Missingness
Dimension reduction plays a pivotal role in analysing high-dimensional data.
Similarity measure for sparse time course data based on Gaussian processes
We propose a similarity measure for sparsely sampled time course data in the form of a log-likelihood ratio of Gaussian processes (GP).
A new approach for generation of generalized basic probability assignment in the evidence theory
To address multi-source information fusion problem, this paper considers the situation of uncertain information modeling from the closed world to the open world assumption and studies the generation of basic probability assignment (BPA) with incomplete information.
Graph-based data clustering via multiscale community detection
We present a graph-theoretical approach to data clustering, which combines the creation of a graph from the data with Markov Stability, a multiscale community detection framework.
