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Found 30 papers, 20 papers with code
Learning to Navigate in Synthetically Accessible Chemical Space Using Reinforcement Learning
In this work, we propose a novel reinforcement learning (RL) setup for drug discovery that addresses this challenge by embedding the concept of synthetic accessibility directly into the de novo compound design system.
Does DetectGPT Fully Utilize Perturbation? Bridge Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better
Hence, we propose a novel fine-tuned detector, Pecola, bridging metric-based and fine-tuned detectors by contrastive learning on selective perturbation.
Unsupervised Discovery of Steerable Factors When Graph Deep Generative Models Are Entangled
Deep generative models (DGMs) have been widely developed for graph data.
A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics
We show the efficiency and effectiveness of NeuralMD, with a 2000$\times$ speedup over standard numerical MD simulation and outperforming all other ML approaches by up to 80% under the stability metric.
A quatum inspired neural network for geometric modeling
By conceiving physical systems as 3D many-body point clouds, geometric graph neural networks (GNNs), such as SE(3)/E(3) equivalent GNNs, have showcased promising performance.
Molecule Joint Auto-Encoding: Trajectory Pretraining with 2D and 3D Diffusion
The fundamental building block for drug discovery is molecule geometry and thus, the molecule's geometrical representation is the main bottleneck to better utilize machine learning techniques for drug discovery.
Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials
Artificial intelligence for scientific discovery has recently generated significant interest within the machine learning and scientific communities, particularly in the domains of chemistry, biology, and material discovery.
ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
This research sheds light on the potential of ChatGPT and conversational LLMs for drug editing.
A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
Meanwhile, existing molecule multi-modal pretraining approaches approximate MI based on the representation space encoded from the topology and geometry, thus resulting in the loss of critical structural information of molecules.
GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning
We pretrain GIMLET on the molecule tasks along with instructions, enabling the model to transfer effectively to a broad range of tasks.
A Text-guided Protein Design Framework
Current AI-assisted protein design mainly utilizes protein sequential and structural information.
Improving Domain Generalization with Domain Relations
Distribution shift presents a significant challenge in machine learning, where models often underperform during the test stage when faced with a different distribution than the one they were trained on.
Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing
Here we present a multi-modal molecule structure-text model, MoleculeSTM, by jointly learning molecules' chemical structures and textual descriptions via a contrastive learning strategy.
Flaky Performances when Pretraining on Relational Databases
We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs extracted from relational databases (RDBs).
Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching
Further by leveraging an SE(3)-invariant score matching method, we propose GeoSSL-DDM in which the coordinate denoising proxy task is effectively boiled down to denoising the pairwise atomic distances in a molecule.
Evaluating Self-Supervised Learning for Molecular Graph Embeddings
Graph Self-Supervised Learning (GSSL) provides a robust pathway for acquiring embeddings without expert labelling, a capability that carries profound implications for molecular graphs due to the staggering number of potential molecules and the high cost of obtaining labels.
An Empirical Study of Retrieval-enhanced Graph Neural Networks
Graph Neural Networks (GNNs) are effective tools for graph representation learning.
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.
Structured Multi-task Learning for Molecular Property Prediction
However, in contrast to other domains, the performance of multi-task learning in drug discovery is still not satisfying as the number of labeled data for each task is too limited, which calls for additional data to complement the data scarcity.
TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery
However, lacking domain knowledge (e. g., which tasks to work on), standard benchmarks and data preprocessing pipelines are the main obstacles for machine learning researchers to work in this domain.
Pre-training Molecular Graph Representation with 3D Geometry
However, the lack of 3D information in real-world scenarios has significantly impeded the learning of geometric graph representation.
Attentive Walk-Aggregating Graph Neural Networks
Our experiments demonstrate the strong performance of AWARE in graph-level prediction tasks in the standard setting in the domains of molecular property prediction and social networks.
Interpreting Molecule Generative Models for Interactive Molecule Discovery
In this work, we develop a simple yet effective method to interpret the latent space of the learned generative models with various molecular properties for more interactive molecule generation and discovery.
Multi-task Learning with Domain Knowledge for Molecular Property Prediction
In this paper, we study multi-task learning for molecule property prediction in a different setting, where a relation graph between different tasks is available.
BERT4SO: Neural Sentence Ordering by Fine-tuning BERT
Sentence ordering aims to arrange the sentences of a given text in the correct order.
Neural Sentence Ordering Based on Constraint Graphs
Our experiments on five benchmark datasets show that our method outperforms all the existing baselines significantly, achieving a new state-of-the-art performance.
Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
Over the last decade, there has been significant progress in the field of machine learning for de novo drug design, particularly in deep generative models.
Bad Global Minima Exist and SGD Can Reach Them
Several works have aimed to explain why overparameterized neural networks generalize well when trained by Stochastic Gradient Descent (SGD).
N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
This paper introduces the N-gram graph, a simple unsupervised representation for molecules.
Ranked #3 on
Molecular Property Prediction
on QM7
ATOMO: Communication-efficient Learning via Atomic Sparsification
We present ATOMO, a general framework for atomic sparsification of stochastic gradients.
