NeurIPS 2024PastGenomics
NeurIPS 2024 Workshop on AI for New Drug Modalities
AIDrugX
- Submission deadline
- Oct 2, 2024, 14:00 UTCimported from OpenReview — check the website for extensions
- Submission portal
- OpenReview
- Notes
- Topics were auto-suggested and may be imprecise — edits welcome.
Accepted papers (111)
Fetched from OpenReview (v2) on 2026-06-10.
3D Interaction Geometric Pre-training for Molecular Relational Learning
A Deep Generative Model for the Design of Synthesizable Ionizable Lipids
A Deep Learning Approach for RNA-Compound Interaction Prediction with Binding Site Interpretability
A Foundational Multi-Modal Knowledge Graph for Pancreatic Cancer Drug Effects Prediction
A Large-Scale Foundation Model for RNA Function and Structure Prediction
Accurate and General DNA Representations Emerge from Genome Foundation Models at Scale
Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation space
Alignment-based and protein foundation models for viral evolution, vaccines and vectors
AlphaFold3, a secret sauce for predicting mutational effects on protein-protein interactions
An Efficient Tokenization for Molecular Language Models
Antibody Library Design by Seeding Linear Programming with Inverse Folding and Protein Language Models
Applications of Modular Co-Design for De Novo 3D Molecule Generation
AptaBLE: A Deep Learning Platform for SELEX Optimization
Bayesian Optimization of Antibodies Informed by a Generative Model of Evolving Sequences
Benchmarking Transcriptomics Foundation Models for Perturbation Analysis : one PCA still rules them all
Best Practices for Multi-Fidelity Bayesian Optimization in Materials and Molecular Research
Beyond Sequence: Impact of Geometric Context for RNA Property Prediction
BindingGYM: A Large-Scale Mutational Dataset Toward Deciphering Protein-Protein Interactions
Bridging the Gap between Database Search and \emph{De Novo} Peptide Sequencing with SearchNovo
CancerFoundation: A single-cell RNA sequencing foundation model to decipher drug resistance in cancer
Cell ontology guided transcriptome foundation model
Chain-of-thoughts for molecular understanding
Computational Antigen Optimization through Symbolic Optimization and Affinity Maturation Simulation
Correlational Lagrangian Schrodinger Bridge: Learning Dynamics with Population-Level Regularization
Deep Interactions for Multimodal Molecular Property Prediction
DeepADAR: A deep learning approach to model regulatory elements of ADAR-based RNA editing and its application to gRNA design
DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning
Derivative-Free Guidance in Continuous and Discrete Diffusion Models with Soft Value-Based Decoding
Designing DNA With Tunable Regulatory Activity Using Discrete Diffusion
Detection of RNA Editing Sites by GPT Fine-tuning
DiffER: Categorical Diffusion Models for Chemical Retrosynthesis
Directly Optimizing for Synthesizability in Generative Molecular Design using Retrosynthesis Models
Discrete Diffusion Schrödinger Bridge Matching for Graph Transformation
Disentangling the Peptide Space: A Contrastive Approach with Wasserstein Autoencoders
Distilling Structural Representations into Protein Sequence Models
Diverse Genomic Embedding Benchmark for functional evaluation across the tree of life.
Effective Protein-Protein Interaction Exploration with PPIretrieval
EnzymeFlow: Generating Reaction-specific Enzyme Catalytic Pockets through Flow Matching and Co-Evolutionary Dynamics
Epitope Generation for Peptide-based Cancer Vaccine using Goal-directed Wasserstein Generative Adversarial Network with Gradient Penalty
Evaluating synergies among generative design models for multi-objective optimization of drug-like proteins
Exploring Log-Likelihood Scores for Ranking Antibody Sequence Designs
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design
FluxGAT: Integrating Flux Sampling with Graph Neural Networks for Unbiased Gene Essentiality Classification
Foundational Model-aided Automatic High-throughput Drug Screening Using Self-controlled Cohort Study
GeneGench: Systematic Evaluation of Genomic Foundation Models and Beyond
Generalized Flow Matching for Transition Dynamics Modeling
Generative Flows on Synthetic Pathway for Drug Design
Generative Model for Synthesizing Ionizable Lipids: A Monte Carlo Tree Search Approach
Geometry-text Multi-modal Foundation Model for Reactivity-oriented Molecule Editing
GFlowNet Pretraining with Inexpensive Rewards
GNNAS-Dock: Budget Aware Algorithm Selection with Graph Neural Networks for Molecular Docking
Harnessing Preference Optimisation in Protein LMs for Hit Maturation in Cell Therapy
HELM: Hierarchical Encoding for mRNA Language Modeling
Homomorphism Counts as Structural Encodings for Molecular Property Prediction
IgBlend: Unifying 3D Structure and Sequence for Antibody LLMs
Improved Off-policy Reinforcement Learning in Biological Sequence Design
Improving Antibody Design with Force-Guided Sampling in Diffusion Models
Improving Molecular Graph Generation with Flow Matching and Optimal Transport
Improving Structural Plausibility in 3D Molecule Generation via Property-Conditioned Training with Distorted Molecules
Interpretable Causal Representation Learning for Biological Data in the Pathway Space
JAMUN: Transferable Molecular Conformational Ensemble Generation with Walk-Jump Sampling
Language Models for Text-guided Protein Evolution
Latent Diffusion Models for Controllable RNA Sequence Generation
LatentDE: Latent-based Directed Evolution accelerated by Gradient Ascent for Protein Sequence Design
Learning Molecular Representation in a Cell
Learning multi-cellular representations of single-cell transcriptomics data enables characterization of patient-level disease states
Learning Protocols for Non-Equilibrium Conformational Free-Energy Estimation Using Optimal Transport and Conditional Flow Matching
Learning to refine domain knowledge for biological network inference
Leveraging Disease-Specific Topologies and Counterfactual Relationships in Knowledge Graphs for Inductive Reasoning in Drug Repurposing
LLMs are Highly-Constrained Biophysical Sequence Optimizers
Machine learning enables engineering of potent, specific, and therapeutically developable proteases
MeMDLM: De Novo Membrane Protein Design with Masked Discrete Diffusion Protein Language Models
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
Mixture of Experts Enable Efficient and Effective Protein Understanding and Design
ML-driven design of 3’ untranslated regions for mRNA stability
Modeling CAR Response at the Single-Cell Level Using Conditional OT
Modeling Complex System Dynamics with Flow Matching Across Time and Conditions
Modeling variable guide efficiency in pooled CRISPR screens with ContrastiveVI+
MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks
Molecular Generation with State Space Sequence Models
MolKD: Distilling Cross-Modal Knowledge in Chemical Reactions for Molecular Property Prediction
Molphenix: A Multimodal Foundation Model for PhenoMolecular Retrieval
MV-CLAM: Multi-View Molecular Interpretation with Cross-Modal Projection via Language Model
Natural Language Prompts Guide the Design of Novel Functional Protein Sequences
Orthrus: Towards Evolutionary and Functional RNA Foundation Models
PepDoRA: A Unified Peptide Language Model via Weight-Decomposed Low-Rank Adaptation
PertEval-scFM: Benchmarking Single-Cell Foundation Models for Perturbation Effect Prediction
PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis
Pharmacophore-based design by learning on voxel grids
PLMFit: Benchmarking Transfer Learning with Protein Language Models for Protein Engineering
PQA: Zero-shot Protein Question Answering for Free-form Scientific Enquiry with Large Language Models
Probing the Embedding Space of Protein Foundation Models through Intrinsic Dimension Analysis
ProtPainter: Draw or Drag Protein via Topology-guided Diffusion
Reinforcement Learning for Enhanced Targeted Molecule Generation Via Language Models
Representation Learning based Target Discovery from UKBB MRI data
Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation
Scaling Dense Representations for Single Cell Gene Expression with Transcriptome-Scale Context
Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics
Similarity-Quantized Relative Difference Learning for Improved Molecular Activity Prediction
Small-cohort GWAS discovery with AI over massive functional genomics knowledge graph
SmileyLlama: Modifying Large Language Models \\for Directed Chemical Space Exploration
SMORE-DRL: Scalable Multi-Objective Robust and Efficient Deep Reinforcement Learning for Molecular Optimization
Structure Language Models for Protein Conformation Generation
TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation
TCRGenesis: Generation of SIINFEKL-specific T-cell receptor sequences using autoregressive Transformer
Training-Free Guidance with Applications to Protein Engineering
TrialDura: Hierarchical Attention Transformer for Interpretable Clinical Trial Duration Prediction
Understanding Protein-DNA Interactions by Paying Attention to Protein and Genomics Foundation Models
Understanding the Sources of Performance in Deep Drug Response Models Reveals Insights and Improvements
Video Representation Learning of Cardiac MRI for Genetic Discovery
Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery