What “Affinity Determination” Means Affinity determination is the process of quantifying how strongly two molecules bind to each other—commonly protein–protein, antibody–antigen, receptor–ligand, or protein–small molecule interactions. In most bioscience and drug discovery contexts, affinity is summarized by the equilibrium dissociation constant (KD): Lower KD = higher affinity (tighter binding). KD is an equilibrium quantity, meaning it reflects the balance between binding and unbinding at steady state. A related way to express the same concept is the association constant (KA), where KA = 1 / KD. The Core Parameters: KD, KA, kon, koff Affinity can be described in two complementary ways: 1) Equilibrium view (how much binds at steady state) KD (M): concentration at which half of binding sites are occupied in a simple 1:1 interaction model. KA (M⁻¹): binding strength as an association constant (inverse of KD). 2) Kinetic view (how fast binding happens) Many instruments determine affinity by measuring rates: kon (M⁻¹·s⁻¹): association/on-rate (how quickly complex forms) koff (s⁻¹): dissociation/off-rate (how quickly complex falls apart) For a 1:1 interaction: KD = koff / kon (at equilibrium). Surface-based biosensors often estimate affinity by extracting these rates from real-time binding curves. Why Affinity Determination…
Aptamers are short single-stranded DNA or RNA molecules that fold into 3D shapes capable of binding specific targets (proteins, small molecules, cells) with high affinity and selectivity. The classic way to discover them is SELEX(Systematic Evolution of Ligands by EXponential enrichment): iterative rounds of binding, partitioning, amplification, and re-selection. What changed the field is high-throughput sequencing (HT-SELEX)—sequencing pools after each round—turning SELEX into a data-rich optimization problem where bioinformatics is no longer optional but central to identifying true binders, understanding enrichment dynamics, and avoiding artifacts. This article explains how bioinformatics for aptamer selection works end-to-end, what signals to extract from sequencing data, how to connect sequence to structure and function, and where modern machine learning fits—without relying on external case studies or outbound links. 1) Why Bioinformatics Matters in Aptamer Selection Traditional SELEX often ends with testing a handful of sequences from late rounds. HT-SELEX changes the game by giving you: Population-level visibility: you can track millions of sequences across rounds, not just a few clones. Early discovery: promising families can emerge before the pool looks “clean,” enabling earlier decision-making and fewer wet-lab rounds when combined with modeling. Artifact detection: PCR bias, sequencing errors, and “sticky” motifs can…
CUSTOM APTAMER DISCOVERY & DEVELOPMENT is the process of creating target-specific single-stranded DNA or RNA aptamers—short nucleic acids that fold into 3D shapes capable of binding proteins, small molecules, cells, vesicles, or other targets with antibody-like selectivity. Most custom programs rely on SELEX (Systematic Evolution of Ligands by EXponential enrichment), then refine “hits” into robust, application-ready binders through sequencing-driven analysis and post-selection optimization. 1) What Aptamers Are (and Why They’re Used) Aptamers are typically ~15–90 nucleotides long and can be engineered to bind targets across a wide size range (from small molecules to whole cells). They’re attractive because they are chemically synthesized (batch-to-batch consistency), can be readily labeled (fluorophores, biotin, etc.), and are generally thermally stable and re-foldable—features that often simplify assay development and manufacturing. Common aptamer use cases Diagnostics & biosensors (capture probes, signal transducers, point-of-care formats) Targeted delivery & therapeutics research (cell-directed binding, payload delivery concepts) Affinity purification & analytical workflows (pull-downs, enrichment, separations) 2) The Core Workflow in Custom Aptamer Discovery A custom program is best thought of as a pipeline with four linked decisions: target format → selection strategy → analytics → optimization. Step A — Target Definition and “Bindability” Planning…
“Completion of SELEX” refers to the point in the Systematic Evolution of Ligands by EXponential enrichment (SELEX)workflow where iterative selection rounds have produced an enriched nucleic-acid pool (DNA or RNA) that contains high-affinity, high-specificity binding sequences (aptamers) for a defined target, and further rounds provide diminishing improvements. In practical terms, completion is less a single universal round number and more a decision point supported by enrichment evidence, performance metrics, and downstream readiness. 1) SELEX in One Picture (Why “Completion” Exists at All) SELEX is an iterative evolutionary loop performed in vitro: Start with a diverse library (randomized nucleic-acid sequences). Bind the library to a target (protein, small molecule, cell surface, complex mixture, etc.). Partition: separate binders from non-binders (the critical “selection” step). Elute and amplify the binders (PCR for DNA; RT-PCR for RNA). Repeat with increasing stringency (less target, harsher washes, counter-selection, etc.). “Completion” matters because every additional round costs time, introduces amplification bias, and can over-enrich “fast amplifiers” rather than “best binders.” Modern practice treats completion as an optimization endpoint, not a ritual number of rounds. 2) What “Completion of SELEX” Typically Means (Conceptual Definition) A common knowledge-centered definition is: The pool has converged toward one…
What “SELEX Aptamer Selection” Means SELEX stands for Systematic Evolution of Ligands by Exponential Enrichment. In plain terms, SELEX aptamer selectionis an iterative laboratory workflow that starts with a huge pool of random DNA or RNA sequences and repeatedly enriches the fraction that binds a chosen target with high affinity and specificity. The “winners” are called aptamers—single-stranded nucleic acids that fold into 3D shapes capable of target recognition, often compared to “chemical antibodies,” but made by selection and synthesis rather than immune systems. Core Concept: Darwinian Evolution in a Test Tube SELEX is essentially variation + selection + amplification: Variation: Begin with a randomized oligonucleotide library (often ~10^13–10^16 unique sequences). Selection: Expose the library to the target; keep sequences that bind. Amplification: PCR (or RT-PCR for RNA workflows) amplifies binders to create the next-round pool. Increasing stringency: Each round tightens conditions (less target, harsher washes, more competitors), enriching the best binders over multiple cycles. Most conventional SELEX workflows run multiple rounds (often roughly 6–15) before candidates are sequenced and characterized. The Classic SELEX Workflow (Step-by-Step, With the “Why”) 1) Library design (the “starting universe”) A typical library contains: A random region (e.g., N30–N60) that can…
“Diagnostics and Therapeutics” is the paired engine of modern healthcare: diagnostics generate actionable evidence about what is happening in the body, and therapeutics use that evidence to choose (and adjust) interventions that improve outcomes. As medicine becomes more data-rich—through molecular testing, advanced imaging, and continuous monitoring—the relationship between diagnostics and therapeutics is shifting from a linear “test-then-treat” workflow to a dynamic feedback loop that refines decisions over time. 1) What “Diagnostics” Means (Beyond Simply Naming a Disease) In clinical practice, diagnostics refers to the tools and methods used to detect, characterize, and track disease-related signals. Importantly, diagnostics is not a single test—it’s a system of evidence that supports decisions across the entire care pathway: Screening diagnostics: detect risk or early disease signals before symptoms are obvious. Diagnostic confirmation: distinguish between conditions with similar presentations. Prognostic diagnostics: estimate likely disease course and severity. Predictive diagnostics: forecast whether a patient is likely to benefit from a specific therapy (a key concept in precision medicine). Monitoring diagnostics: measure response, relapse, or adverse effects over time, enabling treatment adjustment. Major diagnostic categories used today Clinical laboratory diagnostics (blood, urine, tissue, etc.) and medical imaging are foundational, but the fastest-growing…
Aptamers are short, single-stranded nucleic acid molecules (DNA or RNA) that fold into specific 3D shapes and bind targets with high affinity and selectivity—often compared to how antibodies recognize antigens, but built from nucleic acids rather than proteins. Unlike a “generic DNA strand,” an aptamer’s function comes from structure: loops, stems, bulges, pseudoknots, and other motifs that create a binding surface matching a target’s geometry and chemistry. Targets can include proteins, peptides, small molecules, ions, and even whole cells (depending on the selection strategy). Why Aptamers Matter (and How They Differ From Antibodies) Aptamers are often described as “chemical antibodies,” but the differences are exactly why they’re valuable. Key advantages frequently highlighted Low immunogenicity (reduced risk of provoking immune responses) High stability and the ability to refold after denaturation in many cases Easy chemical synthesis (batch consistency, scalable manufacturing) Straightforward modification (labels, linkers, immobilization handles) Trade-offs you should know Nuclease sensitivity (especially RNA aptamers) can be a limitation in biological fluids unless stabilized. Selection bias can occur during discovery (e.g., PCR bias), meaning “best in the tube” isn’t always “best in reality.” Very high affinity does not automatically guarantee best real-world specificity; selection conditions matter. …
Peptide-Drug Conjugates (PDCs) are targeted therapeutics that chemically link a biologically active drug (“payload”) to a peptide that guides the payload toward a specific receptor, microenvironment, or cellular compartment. Conceptually, PDCs resemble Antibody–Drug Conjugates (ADCs), but replace the antibody with a peptide, aiming to keep targeting precision while improving tissue penetration, manufacturing accessibility, and design flexibility. 1) What Exactly Is a PDC (and Why It Matters)? A typical PDC is built from three modular parts: Targeting peptide (the “homing” component) Linker (the chemical bridge that controls stability and payload release) Payload (cytotoxic drug, radionuclide, or other potent therapeutic) This modular architecture allows researchers to tune the PDC for: circulation stability, selective tissue uptake, cellular internalization, controlled release, and overall safety profile. Why it matters: modern drug discovery increasingly values precision delivery—getting more active agent to diseased tissue while reducing exposure to healthy tissue. PDCs are one of the main “next-generation” strategies being explored to push this idea further. 2) PDCs vs ADCs: Same Strategy, Different Vehicle Both PDCs and ADCs aim to deliver potent therapeutics using a targeting moiety + a linker + a payload. The difference is the targeting “vehicle”: ADCs: antibody-based targeting (large proteins)…
Venture capital interest in AI-driven drug discovery has moved from “promise” to a more disciplined phase of investment & funding. Capital is still available, but it increasingly concentrates in teams and platforms that can prove (1) credible biology, (2) proprietary data advantage, and (3) a realistic path to value creation—whether through licensing, partnerships, or clinical progression. Recent industry tracking highlights a rebound in AI funding within drug R&D and emphasizes that “discovery engines” have captured a significant share of investment attention. This article explains the category in a knowledge-first way: what investors look for, common funding structures, due diligence priorities, and how startups can position themselves to raise responsibly. 1) Why This Category Attracts VC: The Economic Logic of “R&D Compression” Drug discovery is slow, high-attrition, and data-hungry. AI’s venture thesis is not “AI finds drugs automatically,” but that it can compress iteration loops and increase decision quality: Cycle-time reduction: faster design–make–test–analyze loops can reduce time to candidate selection. Higher information density: better prioritization of targets, compounds, or modalities can cut dead ends earlier. Platform scalability: once a system is built, it can (in principle) run multiple programs, partners, or indications. Investment trackers have described renewed…
Peptides sit in a sweet spot between small molecules and biologics: they can be engineered for high specificity, tuned with chemical modifications, and explored rapidly through libraries. But peptide screening is not “just HTS with different molecules.” It blends chemistry (library design and synthesis), biology (assay selection and target context), and analytics (MS-based confirmation, binding kinetics, stability, and sometimes regulated bioanalysis). That is why many teams partner with a Contract Research Organization (CRO) for Peptide Screening—to industrialize the workflow from idea → hits → optimized leads, while keeping data quality, reproducibility, and documentation strong. Below is a knowledge-focused overview of what peptide-screening CROs typically do, the major screening technologies, the deliverables you should expect, and the technical “gotchas” that often decide whether a campaign succeeds. 1) What a “CRO for Peptide Screening” actually provides (beyond bench capacity) A peptide-screening CRO usually covers some combination of these pillars: Library strategy + synthesis execution Peptide discovery begins with what you choose to search. Many CROs help design libraries for the biological question (agonist vs antagonist, surface binder vs enzyme substrate, linear vs cyclic peptides, inclusion of non-natural amino acids, etc.), then manufacture the library and track identities and…