Blog — AI Drug Discovery, Docking, MD and Protein Design

Articles from LiteFold on AI drug discovery, molecular docking, molecular dynamics, toxicology, protein design and structural biology.

• Introducing Hybrid Scientific Intelligence Runtime: HSIR packages GPU and CPU workflows, an agentic harness, secure sandboxes, an inference engine and a policy layer into one runtime that runs on infrastructure the customer owns, benchmarked against Modal, Google Cloud Run, Cloudflare, Daytona, E2B, Vercel and Runloop.
• Proteins Aren't Sentences: Why Bigger Protein Models Don't Win: A protein language model (PLM) turns an amino-acid sequence into vectors that can be reused for downstream protein tasks: stability, localization, binding, mutation fitness, evolutionary structure, and more. In the frozen-embedding setting, the pretrained PLM is not fine-tuned. We extract a…
• Environmental Modulation and Ligand-Induced Stabilization of β2-Adrenergic Receptor: The human β2-adrenergic receptor (β2AR) is a cornerstone of modern molecular pharmacology and the prototypical member of the Class A G protein-coupled receptor (GPCR) superfamily. Historically, structural understanding of this receptor was limited to the static snapshots provided by X-ray…
• AminoWeb: Crystallizing the Web for the Finest Protein Data at Scale: Protein machine learning is no longer limited by model architecture alone. It is limited, just as often, by whether the data means what we think it means.
• Rational Design of a Covalent EGFR T790M Inhibitor Using LiteFold: The rise of precision oncology has been driven by the understanding that specific genetic mutations can directly control cancer growth and survival. Targeted cancer therapy has greatly improved outcomes in non-small cell lung cancer (NSCLC), especially through inhibition of the Epidermal Growth…
• Improving Binding Precision of Therapeutic Antibodies with Rosalind by LiteFold: 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…
• Ensemble Docking vs Static docking. When Does Protein Flexibility Matter?: 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 Generative Geometric Turn in AI Drug Discovery: 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…
• Molecular Docking vs. QSAR: How Smart Computing Shapes ADMET Decisions: 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, or cause heart issues no team wants to explain in a…
• Structural Plasticity in the Mutome: Mechanisms of Binding Pocket Alteration and Therapeutic Intervention: 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 adjust its pressure and shape in response to…
• Small Molecule vs Peptide Competition for the Same Pocket: 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.
• Generative models in designing novel scaffolds: 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…
• When Physics meet AI: 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…
• The overlooked role of intrinsic water in protein–ligand binding: 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.
• Fail Fast, Fail Cheap: In-Silico Toxicology Pipelines for Early Drug Candidate: 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 in late-stage…
• Molecular Simulations: The Fun Way to Predict Binding Affinity: 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…
• Molecular Docking in Drug Discovery: Imagine spending over a decade and billions of dollars chasing a single medicine, only to see most candidates fail before they ever reach a patient’s hands. That’s the reality of drug development today. On average, it takes 12 to 15 years and billion of dollars to bring a new drug from the lab…
• Structure Based Drug Design just got easier than ever: We present LiteFold DeNovo, our second flagship feature, a highly efficient structure based drug design workflow to accelerate your lead candidate research to the next level. In this blog, we will discuss the features that are available today, how to get started, and the new features that are…
• Edit, predict, evaluate your proteins structures in bulk with LiteFold: We just launched something new at LiteFold, a structure prediction editor. Yes, an editor, not just a prediction tool. Let me explain.
• Structural biology and AlphaFold: Structural biology delves into fundamental biological structures such as proteins, DNA, and RNA, examining their behaviors, formations, and conformations. The integration of structural biology with AI has opened numerous avenues, including predicting 3D structures of proteins and simulating…