Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
094
Nano Banana
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
015
Nano Banana
A studio-lit chemistry lab with three students, each from different descents: Caucasian, Asian, and Black. They are interacting with vivid, colorful holograms depicting various chemical structures and molecules. The scene is evenly lit with no harsh shadows or contrast, giving a clear and detailed view of the environment. The image has the crisp, clean quality characteristic of a photo taken with a Canon EOS R3 using a 50mm lens, showcasing the advanced technology and educational setting.
4232
Midjourney
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
015
Nano Banana
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
094
Nano Banana
A studio-lit chemistry lab with three students, each from different descents: Caucasian, Asian, and Black. They are interacting with vivid, colorful holograms depicting various chemical structures and molecules. The scene is evenly lit with no harsh shadows or contrast, giving a clear and detailed view of the environment. The image has the crisp, clean quality characteristic of a photo taken with a Canon EOS R3 using a 50mm lens, showcasing the advanced technology and educational setting.
4232
Midjourney
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
015
Nano Banana
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
094
Nano Banana
A studio-lit chemistry lab with three students, each from different descents: Caucasian, Asian, and Black. They are interacting with vivid, colorful holograms depicting various chemical structures and molecules. The scene is evenly lit with no harsh shadows or contrast, giving a clear and detailed view of the environment. The image has the crisp, clean quality characteristic of a photo taken with a Canon EOS R3 using a 50mm lens, showcasing the advanced technology and educational setting.
4232
Midjourney
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
015
Nano Banana
A studio-lit chemistry lab with three students, each from different descents: Caucasian, Asian, and Black. They are interacting with vivid, colorful holograms depicting various chemical structures and molecules. The scene is evenly lit with no harsh shadows or contrast, giving a clear and detailed view of the environment. The image has the crisp, clean quality characteristic of a photo taken with a Canon EOS R3 using a 50mm lens, showcasing the advanced technology and educational setting.
4232
Midjourney
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
094
Nano Banana
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
094
Nano Banana
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
015
Nano Banana
A studio-lit chemistry lab with three students, each from different descents: Caucasian, Asian, and Black. They are interacting with vivid, colorful holograms depicting various chemical structures and molecules. The scene is evenly lit with no harsh shadows or contrast, giving a clear and detailed view of the environment. The image has the crisp, clean quality characteristic of a photo taken with a Canon EOS R3 using a 50mm lens, showcasing the advanced technology and educational setting.
4232
Midjourney
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
015
Nano Banana
Scientific infographic illustrating an AI-driven closed-loop framework for virtual molecular library construction, showing the adaptive cycle of “Representation – Generation – Prediction – Feedback”. Central theme: artificial intelligence empowering drug discovery and molecular design. The diagram is a circular workflow structure centered on the AI virtual molecular library system. Left module: Representation Learning, visualized with neural network icons, molecular graphs, protein structures, and amino acid sequence symbols, representing molecular and protein feature embeddings. Upper-right module: Molecular Generation, showing diffusion or VAE-like model generating diverse small molecules, arrows indicating exploration of chemical space, novelty, and synthesizability constraints. Lower-right module: Property Prediction, containing ADMET, activity, and selectivity metrics represented by radar charts or data panels, feeding results back to the representation module to close the loop. Bottom section: Evolution from virtual to drug-like molecular libraries, shown as a smooth gradient arrow with multi-objective optimization icons balancing drug-likeness and diversity. Right-side branch: Pretrained models for new target ligand design, divided into three submodules—small molecule pretraining, protein pretraining, and cross-modal pretraining (protein–ligand interaction)—depicting embedding fusion or contrastive learning in shared latent space. No human figures, only abstract scientific symbols and molecular visuals. Style: flat vector scientific infographic, modern and minimalistic, clear logical flow, smooth connections between modules. Color scheme: blue for AI and representation, orange-yellow for generation, green for prediction; background light gray or white. Typography: clean sans-serif labels, concise annotations. High resolution (≥600 dpi), suitable for journal publication, ultra-clear, balanced layout, professional academic tone.
094
Nano Banana
A studio-lit chemistry lab with three students, each from different descents: Caucasian, Asian, and Black. They are interacting with vivid, colorful holograms depicting various chemical structures and molecules. The scene is evenly lit with no harsh shadows or contrast, giving a clear and detailed view of the environment. The image has the crisp, clean quality characteristic of a photo taken with a Canon EOS R3 using a 50mm lens, showcasing the advanced technology and educational setting.
4232
Midjourney