Search Results for author:
Found 35 papers, 18 papers with code
Quantum-machine-assisted Drug Discovery: Survey and Perspective
Drug discovery and development is a highly complex and costly endeavor, typically requiring over a decade and substantial financial investment to bring a new drug to market.
DrugAgent: Explainable Drug Repurposing Agent with Large Language Model-based Reasoning
Drug repurposing offers a promising avenue for accelerating drug development by identifying new therapeutic potentials of existing drugs.
SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction
In drug discovery, predicting the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of small-molecule drugs is critical for ensuring safety and efficacy.
BioMamba: A Pre-trained Biomedical Language Representation Model Leveraging Mamba
BioMamba builds upon the Mamba architecture and is pre-trained on an extensive corpus of biomedical literature.
TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models
Clinical trials need to recruit a sufficient number of volunteer patients to demonstrate the statistical power of the treatment (e. g., a new drug) in curing a certain disease.
TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets
Clinical trials are pivotal for developing new medical treatments, yet they typically pose some risks such as patient mortality, adverse events, and enrollment failure that waste immense efforts spanning over a decade.
Structure-based Drug Design Benchmark: Do 3D Methods Really Dominate?
Currently, the field of structure-based drug design is dominated by three main types of algorithms: search-based algorithms, deep generative models, and reinforcement learning.
Graph Adversarial Diffusion Convolution
This paper introduces a min-max optimization formulation for the Graph Signal Denoising (GSD) problem.
drGAT: Attention-Guided Gene Assessment of Drug Response Utilizing a Drug-Cell-Gene Heterogeneous Network
To assess the model's interpretability, we conducted a review of drug-gene co-occurrences in Pubmed abstracts in comparison to the top 5 genes with the highest attention coefficients for each drug.
Language Interaction Network for Clinical Trial Approval Estimation
Clinical trial outcome prediction seeks to estimate the likelihood that a clinical trial will successfully reach its intended endpoint.
ClinicalAgent: Clinical Trial Multi-Agent System with Large Language Model-based Reasoning
Large Language Models (LLMs) and multi-agent systems have shown impressive capabilities in natural language tasks but face challenges in clinical trial applications, primarily due to limited access to external knowledge.
TrialDura: Hierarchical Attention Transformer for Interpretable Clinical Trial Duration Prediction
The clinical trial process, a critical phase in drug development, is essential for developing new treatments.
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.
TWIN-GPT: Digital Twins for Clinical Trials via Large Language Model
Clinical trials are indispensable for medical research and the development of new treatments.
Multimodal Clinical Trial Outcome Prediction with Large Language Models
To address these issues, we propose a multimodal mixture-of-experts (LIFTED) approach for clinical trial outcome prediction.
Uncertainty Quantification on Clinical Trial Outcome Prediction
Selective classification, encompassing a spectrum of methods for uncertainty quantification, empowers the model to withhold decision-making in the face of samples marked by ambiguity or low confidence, thereby amplifying the accuracy of predictions for the instances it chooses to classify.
GenoCraft: A Comprehensive, User-Friendly Web-Based Platform for High-Throughput Omics Data Analysis and Visualization
With GenoCraft, researchers and data scientists have access to an array of cutting-edge bioinformatics tools under a user-friendly interface, making it a valuable resource for managing and analyzing large-scale omics data.
Stoichiometry Representation Learning with Polymorphic Crystal Structures
Despite the recent success of machine learning (ML) in materials science, its success heavily relies on the structural description of crystal, which is itself computationally demanding and occasionally unattainable.
Molecular De Novo Design through Transformer-based Reinforcement Learning
In contrast to the traditional RNN-based models, our proposed method exhibits superior performance in generating compounds predicted to be active against various biological targets, capturing long-term dependencies in the molecular structure sequence.
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural sciences.
A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises
This work aims to serve as a valuable resource for understanding the potential and opportunities of HKG in health research.
PyTrial: Machine Learning Software and Benchmark for Clinical Trial Applications
The code is available at https://github. com/RyanWangZf/PyTrial.
Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations
In this paper, we propose a novel method called GODE, which takes into account the two-level structure of individual molecules.
Machine Learning for Synthetic Data Generation: A Review
In light of these challenges, the concept of synthetic data generation emerges as a promising alternative that allows for data sharing and utilization in ways that real-world data cannot facilitate.
Reinforced Genetic Algorithm for Structure-based Drug Design
The neural models take the 3D structure of the targets and ligands as inputs and are pre-trained using native complex structures to utilize the knowledge of the shared binding physics from different targets and then fine-tuned during optimization.
MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design
Recently, with the rapid development of machine learning methods, especially generative methods, molecule design has achieved great progress by leveraging machine learning models to generate candidate molecules.
Differentiable Scaffolding Tree for Molecule Optimization
The structural design of functional molecules, also called molecular optimization, is an essential chemical science and engineering task with important applications, such as drug discovery.
Differentiable Scaffolding Tree for Molecular Optimization
The structural design of functional molecules, also called molecular optimization, is an essential chemical science and engineering task with important applications, such as drug discovery.
Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development
Here, we introduce Therapeutics Data Commons (TDC), the first unifying platform to systematically access and evaluate machine learning across the entire range of therapeutics.
Ranked #4 on
TDC ADMET Benchmarking Group
on tdcommons
HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data
Next, these embeddings will be fed into the knowledge embedding module to generate knowledge embeddings that are pretrained using external knowledge on pharmaco-kinetic properties and trial risk from the web.
MolDesigner: Interactive Design of Efficacious Drugs with Deep Learning
The efficacy of a drug depends on its binding affinity to the therapeutic target and pharmacokinetics.
MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization
Molecule optimization is a fundamental task for accelerating drug discovery, with the goal of generating new valid molecules that maximize multiple drug properties while maintaining similarity to the input molecule.
DeepPurpose: a Deep Learning Library for Drug-Target Interaction Prediction
Accurate prediction of drug-target interactions (DTI) is crucial for drug discovery.
Ranked #2 on
Drug Discovery
on KIBA
CORE: Automatic Molecule Optimization Using Copy & Refine Strategy
The state-of-the-art approaches partition the molecules into a large set of substructures $S$ and grow the new molecule structure by iteratively predicting which substructure from $S$ to add.
Continuous Word Embedding Fusion via Spectral Decomposition
In this paper, we present an efficient method for including new words from a specialized corpus, containing new words, into pre-trained generic word embeddings.
