https://github.com/boun-tabi/biochemical-lms-for-drug-design
공식 코드Exploiting Pretrained Biochemical Language Models for Targeted Drug Design
Papers with Code 아카이브
Drug Design
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/bowen-gao/sbdd_practical_evaluation
공식 코드From Theory to Therapy: Reframing SBDD Model Evaluation via Practical Metrics
https://github.com/bozhenhhu/a-review-of-plms-and-methods-for-protein-structure-prediction
공식 코드Protein Language Models and Structure Prediction: Connection and Progression
https://github.com/bytedance/decompdiff
공식 코드DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design
https://github.com/bz99bz/m-3
공식 코드M$^{3}$-20M: A Large-Scale Multi-Modal Molecule Dataset for AI-driven Drug Design and Discovery
https://github.com/cambridge-mlg/weighted-retraining
공식 코드Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
https://github.com/carlosinator/tabasco
공식 코드TABASCO: A Fast, Simplified Model for Molecular Generation with Improved Physical Quality
https://github.com/celinehocquette/aaai24-maxsynth
공식 코드https://github.com/celinehocquette/ijcai24-joiner
공식 코드https://github.com/celinehocquette/magicpopper
공식 코드https://github.com/chao1224/moleculestm
공식 코드Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing
https://github.com/chao1224/n_gram_graph
공식 코드N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules
https://github.com/choderalab/espaloma-0.3.0-manuscript
공식 코드https://github.com/choderalab/geometry-benchmark-espaloma
공식 코드https://github.com/choderalab/pl-benchmark-espaloma-experiment
공식 코드https://github.com/choderalab/refit-espaloma
공식 코드https://github.com/choderalab/vanilla-espaloma-experiment
공식 코드https://github.com/cieplinski-tobiasz/smina-docking-benchmark
공식 코드We Should at Least Be Able to Design Molecules That Dock Well
https://github.com/coleygroup/rogi
공식 코드Roughness of molecular property landscapes and its impact on modellability
https://github.com/coleygroup/rogi-results
공식 코드Roughness of molecular property landscapes and its impact on modellability
https://github.com/concept-lab/shrec22_proteinligandbenchmark
공식 코드https://github.com/cselab/LED
공식 코드Multiscale Simulations of Complex Systems by Learning their Effective Dynamics
https://github.com/dahuilangda/biocheminsight
공식 코드https://github.com/davide-belli/generative-graph-transformer
공식 코드Image-Conditioned Graph Generation for Road Network Extraction
https://github.com/davide-belli/toulouse-road-network-dataset
공식 코드Image-Conditioned Graph Generation for Road Network Extraction
https://github.com/ddidacus/mol-moe
공식 코드Mol-MoE: Training Preference-Guided Routers for Molecule Generation
https://github.com/dengjianyuan/Survey_AI_Drug_Discovery
공식 코드Artificial Intelligence in Drug Discovery: Applications and Techniques
https://github.com/dmamur/elembert
공식 코드https://github.com/dockstring/dockstring
공식 코드DOCKSTRING: easy molecular docking yields better benchmarks for ligand design
https://github.com/dptech-corp/Uni-Mol
공식 코드Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction
https://github.com/dreamfold/foldflow
공식 코드Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation
https://github.com/drugilsberg/paccmann
공식 코드https://github.com/dunni3/keypoint-diffusion
공식 코드https://github.com/edapinenut/cbgbench
공식 코드CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph
https://github.com/edapinenut/ppflow
공식 코드PPFlow: Target-aware Peptide Design with Torsional Flow Matching
https://github.com/ehoogeboom/e3_diffusion_for_molecules
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.