Skip to main content

ADME PROCESS

 




90% of drug candidates fail during development. It’s time we change the maths!

If you’re in lead optimization or planning IND-enabling work, I can offer a free 48-hour ADMET benchmark on 5 compounds (SMILES) to show signal on your own chemistry—no obligation. We can provide end to end service for drug discovery and clinical stages of drug development.

As the CSO at Prognica Labs, I engage with discovery teams weekly who are facing common industry challenges: unpredictable ADMET liabilities, late-stage toxicity failures, and lengthy synthesis cycles.

We developed Prognica’s AI/ML platform to address these issues. By integrating predictive models directly into workflows, we assist biotechs and pharma companies in reducing synthesis cycles by 40-60% and accelerating hit-to-lead timelines by over 6 months.

Currently, our partners are experiencing the fastest ROI in three key areas:

🔹 ADMET Hit-to-Lead Optimization: Simultaneous optimization of potency and ADMET properties, generating 500-1,000 viable analogs per scaffold.

🔹 DDI Risk Prediction: Over 80% accuracy in predicting CYP450/transporter interactions and clinical DDI magnitude before IND filing.

🔹 Clinical PK Prediction: Highly accurate human PK predictions (clearance, Vd, t1/2) derived directly from preclinical data.

Our capabilities extend beyond standard small molecules, supporting end-to-end R&D from early discovery through clinical-stage development:

🔬 Target ID & Hit Discovery:
• NLP-driven target validation & literature mining
• Phenotypic screening hit ID & target deconvolution
• AI-guided Fragment-Based Drug Design (FBDD)

💊 Novel Modalities & Complex Targets:
• De Novo design of PROTACs and molecular glues
• Generative AI for Macrocycles (Beyond RO5) & Peptide therapeutics
• RNA-targeted small molecule design
• Antibody humanization & affinity maturation

⚙️ Development, Formulation & Clinical Strategy:
• Solid form selection & formulation development AI
• Oral drug absorption prediction (PBPK-ML hybrid)
• AI-powered reaction prediction & retrosynthetic planning
• Drug repurposing & synergistic combination discovery for clinical pipelines

Comments

Popular posts from this blog

Curated Compendium of Drug Discovery

  Drug discovery is a multidisciplinary process that integrates biology, chemistry, pharmacology , and cutting-edge technologies to identify and develop new therapeutic agents. From target identification to lead optimization and clinical evaluation, each stage requires precision, innovation, and collaboration. A curated list of drug discovery resources provides researchers, students, and professionals with a structured pathway to explore advancements, tools, and strategies that shape modern therapeutics. This compilation serves as a gateway to understanding the evolution of drug discovery, recent breakthroughs, and future directions, fostering knowledge-sharing and accelerating translational research. Databases and Chemical Libraries General Compound Libraries DrugBank  - Comprehensive data on approved and investigational drugs. ZINC  - Free compounds for screening. ChemSpider  - Chemical structures and data. DrugSpaceX  - Chemical and biological spaces. Mcule ...

Understanding NMR Spectroscopy and Chemical Shift Ranges for Functional Groups

  Nuclear Magnetic Resonance ( NMR ) spectroscopy is one of the most powerful analytical tools in pharmaceutical chemistry. It helps chemists determine the structure, purity, and chemical environment of molecules by analyzing the behavior of nuclei (commonly ¹H or ¹³C ) when exposed to a strong magnetic field. In proton NMR ( ¹H-NMR ), the chemical shift (δ, in ppm) provides information about the type of hydrogen atoms present in a compound and their surrounding electronic environment. Depending on nearby atoms and functional groups, signals appear in specific regions of the spectrum — often referred to as upfield (shielded, lower δ values) or downfield (deshielded, higher δ values). The image above summarizes the characteristic δ ranges for different functional groups in ¹H-NMR. Let us break it down systematically: 1. Downfield Region (δ 12 – 6 ppm) Hydrogens in this region are strongly deshielded due to electronegative atoms or π-bond systems. Carboxylic Acids (–COOH) : δ 1...

Pushing the boundaries of computational drug discovery at Isomorphic Labs

  The Isomorphic Labs Drug Design Engine (IsoDDE) has unlocked a new frontier in in-silico drug design, representing a significant evolution beyond AlphaFold 3. What IsoDDE delivers: 🔹 Massive accuracy leap on unconstrained structure prediction The engine more than doubles AlphaFold 3's accuracy on extremely challenging protein-ligand prediction tasks — including systems far outside the training distribution. 🔹 Best-in-class binding affinity prediction IsoDDE predicts how strongly small molecules bind to targets with accuracy that exceeds gold-standard physics-based methods, at a fraction of the computational cost and time. 🔹 Blind identification of novel binding pockets Even without existing structural data, the engine reveals previously unseen binding sites — just from an amino acid sequence — enabling drug designers to explore entirely new chemical action spaces. 🔹 Expanded support for complex biologics Beyond small molecules, the engine boosts prediction fidelity for...