Retrosynthesis
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Retrosynthetic analysis is a pivotal synthetic methodology in organic chemistry that employs a reverse-engineering approach, initiating from the target compound and retroactively tracing potential synthesis routes and precursor molecules. This technique proves instrumental in sculpting efficient synthetic strategies for intricate molecules, thus catalyzing the evolution and progression of novel pharmaceuticals and materials.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/DiXue98/PDVN
공식 코드https://github.com/DrugLT/ImDrug
공식 코드ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery
https://github.com/Hanjun-Dai/GLN
공식 코드Retrosynthesis Prediction with Conditional Graph Logic Network
https://github.com/MIRALab-USTC/DD-RetroDCVAE
공식 코드Modeling Diverse Chemical Reactions for Single-step Retrosynthesis via Discrete Latent Variables
https://github.com/QizhiPei/BioT5
공식 코드BioT5+: Towards Generalized Biological Understanding with IUPAC Integration and Multi-task Tuning
https://github.com/aalto-quml/diffalign
공식 코드Equivariant Denoisers Cannot Copy Graphs: Align Your Graph Diffusion Models
https://github.com/ai-hpc-research-team/slm4crp
공식 코드A Self-feedback Knowledge Elicitation Approach for Chemical Reaction Predictions
https://github.com/austint/retro-fallback-iclr24
공식 코드Retro-fallback: retrosynthetic planning in an uncertain world
https://github.com/batistagroup/DirectMultiStep
공식 코드DirectMultiStep: Direct Route Generation for Multi-Step Retrosynthesis
https://github.com/bigchem/synthesis
공식 코드State-of-the-Art Augmented NLP Transformer models for direct and single-step retrosynthesis
https://github.com/catalystforyou/retro-bleu
공식 코드https://github.com/chao1224/chatdrug
공식 코드ChatGPT-powered Conversational Drug Editing Using Retrieval and Domain Feedback
https://github.com/coleygroup/desp
공식 코드Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search
https://github.com/coleygroup/graph2smiles
공식 코드https://github.com/dptech-corp/nag2g
공식 코드https://github.com/fangleigit/RetroSub
공식 코드Leveraging Reaction-aware Substructures for Retrosynthesis Analysis
https://github.com/gmum/retrogfn
공식 코드RetroGFN: Diverse and Feasible Retrosynthesis using GFlowNets
https://github.com/heewoongnoh/retrieval-retro
공식 코드Retrieval-Retro: Retrieval-based Inorganic Retrosynthesis with Expert Knowledge
https://github.com/igashov/retrobridge
공식 코드https://github.com/ilog-ecnu/EvoRRP
공식 코드https://github.com/intelligent-molecular-systems/llm_finetuning_for_biochemistry
공식 코드Leveraging Large Language Models for enzymatic reaction prediction and characterization
https://github.com/john-bradshaw/molecule-chef
공식 코드https://github.com/lauyikfung/t-rex
공식 코드https://github.com/microsoft/DVMP
공식 코드https://github.com/microsoft/syntheseus
공식 코드Retro-fallback: retrosynthetic planning in an uncertain world
https://github.com/mirunacrt/synflownet
공식 코드SynFlowNet: Design of Diverse and Novel Molecules with Synthesis Constraints
https://github.com/ml-jku/mhn-react
공식 코드Modern Hopfield Networks for Few- and Zero-Shot Reaction Template Prediction
https://github.com/molecule-one/megan
공식 코드Molecule Edit Graph Attention Network: Modeling Chemical Reactions as Sequences of Graph Edits
https://github.com/ninglab/g2retro
공식 코드$\mathsf{G^2Retro}$ as a Two-Step Graph Generative Models for Retrosynthesis Prediction
https://github.com/ninglab/rlsync
공식 코드RLSynC: Offline-Online Reinforcement Learning for Synthon Completion
https://github.com/otori-bird/retrosynthesis
공식 코드Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction
https://github.com/qinyuma316/retrosynthesisagent
공식 코드Leveraging Large Language Models as Knowledge-Driven Agents for Reliable Retrosynthesis Planning
https://github.com/schwallergroup/saturn
공식 코드Directly Optimizing for Synthesizability in Generative Molecular Design using Retrosynthesis Models
https://github.com/sfcurre/differ
공식 코드https://github.com/songtaoliu0823/crebm
공식 코드Preference Optimization for Molecule Synthesis with Conditional Residual Energy-based Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.