PostEra

Step 1: Define the target protein and its binding site using structural data.

Step 2: Utilize Proton's AI-driven molecular design to generate a diverse library of potential inhibitors.

Step 3: Employ Proton's activity prediction to prioritize compounds with high predicted binding affinity.

Step 4: Synthesize and test the top-ranked compounds in vitro to validate their inhibitory activity.

Step 1: Input the chemical structure of the lead compound into Proton.

Step 2: Use Proton's structure optimization to identify modifications that improve solubility.

Step 3: Evaluate the predicted impact of the modifications on other relevant properties, such as potency and selectivity.

Step 4: Synthesize and test the optimized compounds to confirm the improved solubility and maintain desired activity.

Step 1: Input the chemical structures of known drugs into Proton.

Step 2: Use Proton's activity prediction to estimate their potential activity against the new target.

Step 3: Prioritize drugs with high predicted activity for further investigation.

Step 4: Conduct in vitro and in vivo studies to validate the repurposing potential of the selected drugs.

Step 1: Input the structural data of the target protein and related family members into Proton.

Step 2: Use Proton's AI-driven molecular design to generate compounds with selectivity for the target protein.

Step 3: Employ Proton's activity prediction to assess the predicted activity of the compounds against all family members.

Step 4: Synthesize and test the most selective compounds to confirm their on-target and off-target activity profiles.

Step 1: Input the chemical structures and activity data of the compound series into Proton.

Step 2: Use Proton's machine learning models to identify key structural features that correlate with activity.

Step 3: Visualize the SAR using interactive plots and diagrams.

Step 4: Use the SAR information to design new compounds with improved activity.