A structure-guided, AI-assisted computational workflow for covalent inhibitor design against a drug-resistant NSCLC mutation The Clinical Imperative: The Evolutionary Arms Race in Non-Small Cell Lung Cancer The rise of precision oncology has been driven by the understanding that specific genetic mutations can directly control cancer growth and survival. Targeted cancer
Disclaimer: This is a purely in-silico case study intended to demonstrate the computational capabilities of the LiteFold platform and its in-house AI co-scientist, Rosalind. None of the designs reported here have been experimentally validated. All claims about "improved" metrics refer to model-internal confidence scores, not to empirical binding
If one were to trust the diagrams found in introductory biology textbooks, molecular recognition would appear to be a serene, orderly, and deterministic affair. The "Lock and Key" model, proposed by Emil Fischer in 1894, depicts the protein as a rigid, Pac-Man-like entity with a mouth the active
The pharmaceutical industry stands at a critical juncture, often described through the lens of "Eroom's Law"—the observation that drug discovery is becoming slower and exponentially more expensive over time, despite aggregate improvements in technology. The process of identifying a therapeutic candidate, optimizing its properties, and
Why ADMET Needs a Better Plan Most drug projects begin with bright hopes and a pile of molecules that look good on paper. Then reality walks in. A compound that seemed like a star in early screens may vanish in the gut, stick to plasma proteins, clog a liver enzyme,
1. From Handshakes to Dynamic Landscapes Imagine a handshake. It seems like a simple gesture, two hands clasping. But consider the nuance. If your hand is rigid like stone, the handshake fails. If it is too limp, the connection is weak. A perfect handshake requires the hand to conform, to
In the high stakes world of modern drug discovery, finding a binding pocket on a disease causing protein is only half the battle. The real challenge and the subject of intense biophysical debate is deciding what kind of "key" should fit that lock. For most of the 20th
Drug discovery in past was like hunting for a rare spice in an endless pantry. Scientists would sift through mountains of existing molecules a “virtual screening” of billions of compounds hoping that one might bind to a disease target. This process has often been compared to finding a needle in
Docking scores offer a quick first estimate, but often miss the underlying thermodynamics that drive real binding. Chemists have been chasing the dream of predicting binding affinity with accuracy for years, yet the gap between computer scores and lab results keeps showing up like that one stubborn error bar that
Water is essential for life, that’s something we all know. But beyond keeping us alive, water also shapes how molecules recognize, interact, and bind to each other. In the world of proteins and ligands, it’s far more than just a background solvent. Inside a protein’s binding pocket,
Drug discovery is basically a casino where the house almost always wins. Around more than 90% of drug projects never make it to patients, and the price of failure climbs steeply the further along you go. Flop early in discovery? That’s about a million dollars down the drain. Flop
Picture a thriller where the hero tracks down the villain using perfect surveillance footage, kicks down the warehouse door, and finds nothing. The target vanished hours ago. This is exactly what happened when our docking algorithm ranked millions of "perfect" binders in an afternoon. The wet lab results?