NanJing GenePioneer

Not every docking pose deserves an MD simulation！！ MD simulation can be powerful. But it should not be used just to make a screening report look more complete. The better question is not: “Can we run MD?” It is: “Will MD change the next decision?” ━━━━━━━━━━━━━━ 🧲 𝗪𝗵𝗲𝗻 𝗠𝗗 𝗮𝗱𝗱𝘀 𝘃𝗮𝗹𝘂𝗲 MD is most useful when docking gives you a plausible hypothesis, but not enough confidence to act on it. For example: • Top hits have similar docking scores   • The binding pocket is flexible   • The pose looks reasonable, but key contacts may not hold   • Water molecules or side-chain movement may affect binding   • One more layer of prioritization is needed before synthesis or testing  In these cases, MD helps move the question from: “Which compound scored well?” to: “Which binding hypothesis stays credible over time?” ━━━━━━━━━━━━━━ ⚠️ 𝗪𝗵𝗲𝗻 𝗠𝗗 𝗺𝗮𝘆 𝗻𝗼𝘁 𝗯𝗲 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗻𝗲𝘅𝘁 𝘀𝘁𝗲𝗽 MD is not a rescue tool for weak inputs. If the docking pose is chemically unrealistic, MD will not make it meaningful. If the compound list is still too large, MD may become an expensive bottleneck. If there is no clear decision to make, the simulation may generate more data without improving the project. And if the only reason to run MD is to include a “dynamic” figure in the report, it is probably too early. ━━━━━━━━━━━━━━ 🧬 𝗪𝗵𝗮𝘁 𝘄𝗲 𝗹𝗼𝗼𝗸 𝗳𝗼𝗿 A useful MD analysis should help answer practical questions: • Does the ligand stay in the pocket?   • Do key interactions persist?   • Does the protein adapt in a way that supports binding?   • Are there unstable poses that should be deprioritized?   • Do similar compounds behave differently enough to guide the next experiment?  The output should not just be a beautiful trajectory. It should help the team decide what to test, optimize, or set aside. ━━━━━━━━━━━━━━ 🧭 𝗢𝘂𝗿 𝘃𝗶𝗲𝘄 At GenePioneer, we see MD simulation as a decision layer — not a default add-on. It works best after docking has narrowed the field and before the team commits more time and budget to synthesis, assays, or lead optimization. Used well, MD can help clarify binding stability, protein flexibility, and interaction quality. Used too early, it can slow the project down. The goal is not to run the most simulations. The goal is to run the simulations that answer the right question. 📩 Have a shortlist of docking hits and are not sure whether MD is worth the next step? Send us your target, top candidates, and current decision point. We can help assess whether MD simulation will add value — or whether another validation step should come first. #GenePioneer #MolecularDynamics #DrugDiscovery #MolecularDocking #VirtualScreening #CADD #ComputationalChemistry #LeadOptimization #AIDrugDiscovery

📌 A better hit starts with a better target！ One quiet risk in early drug discovery is moving too fast from a disease hypothesis to compound screening. The screen may look productive. The ranked list may look impressive. But if the target rationale is weak, even the best virtual screening campaign can end up organizing uncertainty instead of reducing it. Before asking: “Which compounds should we screen?” A better question is: “Is this target ready to be screened?” ━━━━━━━━━━━━━━ 🧬 𝗕𝗶𝗼𝗹𝗼𝗴𝘆   Is the target close enough to the disease? Expression changes can be useful, but rarely enough on their own. A stronger target story connects the target to disease mechanism, pathway behavior, cellular phenotype, or patient-relevant biology. The question is not just: “Is this target different?” It is: “Does changing this target have a plausible reason to change the disease?” ━━━━━━━━━━━━━━ 🧪 𝗧𝗿𝗮𝗰𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆   Can the target actually be worked on? Before investing in compound discovery, it helps to ask: Can the protein be expressed, modeled, or structurally characterized? Is there a binding pocket, functional interface, or degron-like opportunity? Can a reliable assay be built? Can target engagement be measured later? A target does not need to be easy. But it does need a practical path forward. ━━━━━━━━━━━━━━ 📊 𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲   Do multiple signals point in the same direction? The most convincing targets usually do not rely on one data layer. Genetics. Transcriptomics. Proteomics. Spatial biology. Perturbation data. Phenotypic response. No single layer has to be perfect. But the overall direction should be coherent enough to justify the next investment. ━━━━━━━━━━━━━━ 🧭 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗣𝗮𝘁𝗵   What would make a hit meaningful? If screening produces hits, what would you need to see next? Direct binding? Pathway modulation? Cellular target engagement? A disease-relevant phenotype? Early developability signals? A screening campaign is stronger when the validation path is defined before the hit list arrives. ━━━━━━━━━━━━━━ At GenePioneer, we see target evaluation and compound screening as connected decisions. Bioinformatics can clarify biological rationale. Proteomics and spatial proteomics can strengthen target context. Structural analysis can assess tractability. Molecular docking and AI-driven drug design can help prioritize chemical starting points. The goal is not to run every method. The goal is to answer the right question at the right stage. Before investing in a screening campaign, make sure the target is ready for one. 📩 Preparing to screen a target? Send us the target, disease context, and available evidence. We can help assess whether it is ready for computational screening — or whether stronger target validation should come first. #GenePioneer #DrugDiscovery #TargetValidation #VirtualScreening #Bioinformatics #Proteomics #MolecularDocking #AIDrugDiscovery #Biotech #CADD

Virtual screening gives you a ranked list. The real value starts after that！！！ A docking score is not a validated hit. Between “interesting on screen” and “worth advancing,” there is a validation path that decides whether a project gains confidence — or spends months chasing noise. Here is the post-screening workflow we usually think through with teams. ━━━━━━ 🔬 1. Primary assay: start broad Does the compound show measurable activity under the right assay conditions? This may involve enzyme inhibition, competitive binding, TR-FRET, AlphaScreen, or another fit-for-purpose assay. The goal is not to declare winners. It is to remove obvious negatives and identify compounds worth a closer look. ━━━━━━ ⚠️ 2. Counter-screens: catch false positives early This is the step many teams rush. It is also the step that can save the most time. Before investing in chemistry, ask: Does the signal survive detergent or protein additives? Is the dose-response curve clean and reproducible? Any signs of aggregation, interference, or nonspecific binding? A compound that only looks good in one fragile assay condition is not a hit yet. It is a risk signal. ━━━━━━ 🧲 3. Binding confirmation: build real evidence Biochemical activity tells you something happened. Biophysical assays help show whether the compound is actually engaging the target. TSA/DSF can give an early read on stabilization. SPR can reveal kinetics and curve quality. Other orthogonal methods can add confidence when needed. The point is to build confidence before calling a compound a confirmed hit. ━━━━━━ 🧫 4. Cellular validation: test whether the biology holds A compound that binds purified protein does not automatically work in a cell. Permeability, efflux, metabolism, and cellular context can change the outcome. CETSA can help assess target engagement in cells. A pathway or phenotypic assay can then ask: Does that engagement translate into the expected biological effect? If activity disappears in cells, the story is not always over. It may mean the compound needs optimization. ━━━━━━ 🎯 What the funnel really means Virtual screening is not meant to produce perfect leads on day one. Its job is to enrich the pool. Virtual hits → apparent actives → counter-screen survivors → confirmed binders → cellularly active hits → compounds worth advancing The value is not the length of the ranked list. The value is how efficiently that list becomes evidence. ━━━━━━ 🧭 Our view At GenePioneer, we do not treat virtual screening as a standalone report. We design the computational workflow with downstream validation in mind. Cuz the handoff to the wet lab is where the work becomes real. 📩 Moving from virtual screening to experimental validation? Send us your target, assay setup, and screening goal. We can help map the computational strategy to the validation path. #GenePioneer #VirtualScreening #DrugDiscovery #MolecularDocking #HitDiscovery #ADMET #CADD #AIDD

𝟐𝟑𝟓 𝐦𝐢𝐥𝐥𝐢𝐨𝐧 compounds is not the story. The 𝟔𝟖 chosen for testing are! A recent Nature Communications study on GPR139 is a strong reminder of what AI-enabled drug discovery can do well: It helps researchers decide what to test next. Not in theory. Not as a headline. But as part of a real discovery workflow. 🔬 𝐓𝐡𝐞 𝐜𝐚𝐬𝐞 GPR139 is an orphan GPCR with interest in neuropsychiatric research. The challenge was not simply finding “more molecules.” The real challenge was choosing which molecules were worth experimental attention. The researchers started with a high-resolution GPR139 structure and docked 235 million lead-like compounds against the binding site. From that screen, they selected 68 top-ranked compounds for synthesis and testing. 📝 The results: 🧪 5 full agonists identified   📊 Potencies from 160 nM to 3.6 µM   🧬 Structure-guided optimization of a promising scaffold   🔬 Cryo-EM confirmation of the predicted binding mode   🧠 In vivo behavioral effects observed for one optimized agonist  💡 𝐖𝐡𝐚𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 The screen was not the finish line. It was the start of a decision-making loop. Computation narrowed the search space. Functional assays tested whether the predictions held up. Structure-guided optimization improved the scaffold. Cryo-EM helped confirm how the ligand actually bound. That is a more realistic picture of AI in drug discovery. Not “AI replaces experiments.” Not “a model generates a molecule and the work is done.” But a connected workflow where computation makes the next experiment more focused, more informed, and less wasteful. ⚠️ 𝐀 𝐮𝐬𝐞𝐟𝐮𝐥 𝐫𝐞𝐚𝐥𝐢𝐭𝐲 𝐜𝐡𝐞𝐜𝐤 The study also explored whether AlphaFold3 could replace experimental receptor-ligand structures. The result was nuanced. Promising in some contexts, but still limited for understudied GPCRs where binding interactions are hard to predict reliably. That nuance matters. In pharma R&D, the value of AI is not just speed. It is better prioritization. Better hypotheses. Better use of experimental resources. 🧭 𝐇𝐨𝐰 𝐰𝐞 𝐭𝐡𝐢𝐧𝐤 𝐚𝐛𝐨𝐮𝐭 𝐢𝐭 𝐚𝐭 𝐆𝐞𝐧𝐞𝐏𝐢𝐨𝐧𝐞𝐞𝐫 The goal is not to use AI for the sake of using AI. The goal is to reduce uncertainty before the next decision. That may mean: 🔍 Virtual screening to prioritize candidates   🧲 Docking to generate binding hypotheses   🧬 Structure-based design to guide optimization   🧫 MD simulation to assess interaction stability   ⚠️ ADMET and developability assessment to reduce downstream risk  If your team is working on a target and the key question is: “Which compounds should we test first?” Computational screening may be the right place to start. 📩 Send us your target, available structural information, and project goal. We’ll help you map the right computational workflow! #AIDrugDiscovery #DrugDiscovery #VirtualScreening #MolecularDocking #GPCR #Biotech #ComputationalChemistry #ADMET #GenePioneer

🧬 “AI drug design” and “generative AI for molecules” are often used as if they mean the same thing. They don’t ! And in pharma R&D, that distinction can get expensive quickly. When people say “AI can design drugs,” the better question is: Which kind of AI are we talking about? Because predictive AI and generative AI solve different problems at different stages. —— 🔍 Predictive AI asks: “Is this likely to work?” This is where much of the practical value in computational drug discovery still lives. 🧪 Virtual screening   🧲 Molecular docking   📊 Binding affinity prediction   ⚠️ ADMET profiling   🧫 Molecular dynamics simulation  These workflows may not sound as flashy as “AI-generated molecules.” But when the goal is to identify hit candidates, prioritize compounds, or reduce experimental waste, predictive methods often deliver the clearest near-term ROI. They help teams make better decisions before synthesis, assays, and downstream optimization. —— 🧬 Generative AI asks: “What should we make?” This is where de novo molecule generation, peptide design, scaffold hopping, and chemical space exploration come in. It can be powerful when a team needs structural novelty. But generated molecules are not automatically useful molecules. A structure can look promising on screen and still run into real-world problems: 🧱 Difficult synthesis   🧭 Weak generalization   🧪 Poor developability   ⚡ Activity cliffs   ⚖️ PK, safety, and selectivity trade-offs  A model can propose a molecule. That does not mean the molecule can be made, tested, optimized, and advanced. —— 🎯 The real question is not: “Should we use AI?” The real question is: Where is the project bottleneck? Target validation? Generative design will not fix it. Hit identification? Screening and docking may be the better starting point. Lead optimization? Predictive modeling, ADMET profiling, and MD simulation may matter more than novelty. Need a new scaffold, peptide, or macrocyclic candidate? Generative design may be the right tool. —— 🧭 At GenePioneer, we start with the scientific decision the client needs to make next. Many projects begin with predictive workflows: screening, docking, binding prediction, MD simulation, and ADMET assessment. Generative design enters when novelty is truly needed — new macrocyclic peptides, unexplored scaffolds, or targets where no known ligand provides a useful starting point. The strongest teams are not the ones using the most AI. They use the right method at the right stage, for the right problem. So the next time someone says “AI can design drugs,” ask: Predictive, generative, or both? 📩 Working on a target and unsure which approach fits best? Send us your target, modality, and project goal. We’ll share a straightforward view. #AIDrugDiscovery #DrugDiscovery #CADD #MolecularDocking #GenerativeAI #Genepioneer #ComputationalChemistry #VirtualScreening

🧬 You don’t need a full multi-omics dataset to publish multi-omics findings. That surprises a lot of people. But it’s true — and once you understand it, it changes how you design studies when budget is the constraint. Many researchers assume integrative analysis only counts if every omics layer comes from: • the same samples • the same experiment • the same cohort • all generated fresh That is the ideal. But it is also expensive — and often unnecessary. 💡 What actually matters is not experimental identity. It’s biological coherence. If you have: • high-quality proteomics from your own samples • a well-matched public transcriptomics dataset • comparable tissue, cell line, or disease context …then the integration can still be scientifically rigorous. Reviewers are usually asking: 👉 Do these layers tell a coherent biological story? Not: 👉 Did they come from the same Eppendorf tube? 📚 Public omics databases are massively underused. TCGA. GTEx. PRIDE. MetaboLights. GEO. The amount of high-quality public data available for integration is enormous. And yet many researchers still treat these resources as: • background reading • supplementary context • something to cite …instead of treating them as a legitimate analytical layer in their own study. In reality, integrating your novel proteomics with a carefully matched public transcriptomics dataset can be a defensible and publishable strategy — as long as the matching criteria are clearly stated. 🎯 Strategic layering beats comprehensive coverage. One high-confidence layer •one complementary public layer •one clear mechanistic question …will often outperform: ❌ four layers of shallow coverage ❌ weak matching ❌ no real narrative Depth where it matters is usually more valuable than breadth for its own sake. 🔬 The publication happens in the analysis — not in the sample collection. Two researchers can start with essentially the same datasets and end up with completely different papers. Why? Because one analysis reveals: • mechanism • biological logic • a coherent story And the other just produces: • lists of changes • enrichment tables • disconnected results That difference is often what separates a manuscript that feels descriptive from one that feels publishable. ⚠️ This is not a workaround. It is how a significant share of strong multi-omics papers are actually structured. Read the methods sections closely. You will find that many publishable multi-omics studies are not built from every omics layer being generated de novo in the same experiment. They are built from good integration, good matching, and good analysis. 💬 What’s holding back your current project more right now — the data you don’t have, or the analysis of the data you already do? #Research #MultiOmics #Proteomics #PhDLife #Bioinformatics #Genepioneer

🧬 Most teams run molecular dynamics after they’ve already picked their lead. That’s roughly the equivalent of stress-testing a bridge after you’ve already decided where to build the city. In practice, MD simulation is often more valuable earlier — during lead optimization, before significant medicinal chemistry effort has already been committed to a scaffold that may turn out to have a problem. 💡 Here’s what a well-designed MD run can actually tell you at that stage: 🔹 1) Binding stability under dynamics A compound can look excellent in a static docking pose… and then dissociate in under 10 nanoseconds once the protein is actually moving. That’s information you want before you spend three rounds of SAR on a scaffold that is not genuinely bound. 🔹 2) Induced fit and cryptic pockets Crystal structures capture one conformation. Proteins breathe. MD can surface: • alternative binding modes • induced-fit effects • cryptic or allosteric pockets not visible in the crystal structure And some of those become highly relevant when you are trying to improve selectivity. 🔹 3) Water network behavior Structured water in the binding site is often doing more than people realize. MD-based water analysis can help show: • which water molecules your compound is displacing efficiently • which ones remain energetically important • where the molecule may be worth growing next That gives a much clearer read on where optimization effort may actually pay off. 🔹 4) Resistance mutation modeling If your target has known resistance variants, parallel MD on wildtype vs mutant systems can give you an early view of binding stability differences. That means you can start learning about potential resistance liabilities before running a single selectivity assay. ⚖️ What MD changes is not whether validation is needed. It changes which experiments you prioritize — and which scaffolds you walk away from before they consume another six months of chemistry. MD does not replace wet-lab confirmation. But it can absolutely improve: • prioritization • scaffold selection • hypothesis quality during lead optimization 🎯 Used well, MD is not just a confirmation step. It is a decision-making tool. What stage are you currently running MD at in your pipeline? #DrugDiscovery #MolecularDynamics #LeadOptimization #ComputationalChemistry #MedicinalChemistry #AIDD #Genepioneer #MD

🚀 The part of AI drug discovery that actually works is less exciting than the part that gets the press releases. This isn’t cynical. The tools are genuinely powerful. 🧠 What has improved: • AlphaFold2 → enables structure-based work without crystal structures • Generative models → explore chemical space classical methods miss • GPU infrastructure → large-scale docking in days, not months And yet… 📉 What hasn’t really changed: • Phase III pipelines look similar to 2019 • Time to IND hasn’t meaningfully compressed • Phase II attrition remains largely the same 💡 Here’s the honest version: 🧪 AI has improved the front end. • Faster, broader virtual screening • More efficient chemical space exploration • ADMET filtering now standard at hit selection  → permeability  → metabolic stability  → hERG liability 👉 Result: Less wasted assay capacity Better prioritization More efficient use of wet-lab resources 🧬 But the experimental bottleneck is still there. Every generated molecule → needs synthesis Every virtual hit → needs binding validation Every in vitro signal → needs in vivo confirmation 👉 Biology doesn’t compress. 👉 And most failures are biological, not chemical. AI sits upstream of that problem. 📊 Where real ROI actually comes from: The teams getting value don’t ask: ❌ “Can AI design our drug?” They ask: ✅ “Out of 80,000 compounds, which 800 are worth testing this quarter?” 👉 That’s where AI works. 👉 That’s where it holds up in real pipelines. 🎯 Bottom line: ✔ The tools are good ✔ The expectations need to match what they actually do 💬 Where do you see computational methods creating the most real value in your pipeline — and where do they still fall short? #AIDrugDiscovery #DrugDiscovery #ComputationalChemistry #MolecularDocking #Pharmaceutical #AIDD #CADD #GenePioneer

Reviewer 2, Comment 3: “The multi-omics data lacks integrative analysis.” If you’ve seen this line before, you know how frustrating it is. The data is solid. The experiments were well designed. You’ve spent months on the work. And the rejection is not really about the science itself. It’s about how the results were assembled into a story. Earlier this year, a postdoc came to us with a proteomics dataset she had spent 18 months generating: 🔬 TMT-labeled 🔬 10 conditions 🔬 Excellent peptide coverage 🔬 Clean QC throughout Nothing was technically wrong with the data. She had still been rejected twice. The analysis had stopped at a list of differentially expressed proteins ranked by fold change and p-value. That is a result. It is just not yet a finding. What a reviewer sees is: ➡️ a catalogue of things that changed What they want to see is: ➡️ what changed ➡️ why it matters biologically ➡️ what it tells us about mechanism So we went back through the data she already had. No new experiments. No new samples. No re-sequencing. Here’s what changed: 1️⃣ We integrated the transcriptomics data. mRNA–protein correlation immediately split the dataset: • around 40% of significant protein changes tracked with transcript-level changes • the remaining 60% did not That distinction changed the interpretation completely. One side was transcriptionally driven. The other pointed to post-translational regulation. That became the central finding of the revised manuscript. 2️⃣ We rebuilt the pathway analysis. The original version had 23 enriched terms in a supplementary table. Useful for software output. Not useful for a reviewer. We collapsed it into one coherent pathway story: • one upstream signaling event • one core effector complex • two downstream functional consequences Instead of a list, it became a mechanism. 3️⃣ We created one integrative figure. One arm showed the transcriptionally driven changes. The other showed the post-translational changes. Both converged on the same phenotypic outcome. One figure that made the full argument visible in seconds. 📌 The paper was accepted at the third journal, three months after she first reached out. “Lacks integrative analysis” usually does not mean you need more data. More often, it means the story is still buried in the results you already have. What are you currently stuck on? Drop it in the comments. #Research #MultiOmics #Proteomics #PhDLife #PublishOrPerish #ABPP #Proteomics #Genepioneer