OncoBind AI - Cancer & Immunotherapy Drug Discovery Simulator

🔬 Target & Discovery Pipeline

ML READY

Oncology Target Mutation PDB Pocket

ML Screening Engine Algorithm

Virtual Compound Library In-Silico Scale

Binding Affinity Cutoff (Kd) 50 nM

Lower nM = tighter nanomolar target binding

Neural ADMET Toxicity Strictness High

Filters hERG cardiotoxicity & hepatotoxicity

Max SAScore (Synth Accessibility) 4.2

1.0 = trivial synthesis, 10.0 = intractable

Ligand Core H-Bond Donor/Acceptor Hydrophobic Pocket Contact Mutation Residue (G12D) Click/drag canvas to rotate & probe residue forces

📉 High-Throughput ML Funnel Attrition

In-Silico Time: 14.8 seconds

1. Virtual Pool

500,000

100% In-Silico

2. Pocket Docked

12,480

-97.5% Filtered

3. ΔG < -9 kcal

842

-93.2% Eliminated

4. Neural ADMET

-92.4% Tox Filtered

5. Validated Leads

6 Leads

Passed All Gates

🏆 Top In-Silico Lead Candidates

Select row to inspect binding geometry and pharmacological profile

Candidate ID	SMILES / Structure Core	Affinity Kd (nM)	Docking ΔG (kcal/mol)	Off-Target Tox Risk	SAScore	MHC Presentation	Status

📑 Lead Drug Dossier: CAND-8942

Selectivity: 142x over Wild-Type

14.2 nM

Target Affinity Kd

-11.4 kcal

Binding Energy ΔG

2.8 / 10

Synth Accessibility

Predicted Mechanism: Allosteric switch-II covalent entrapment targeting Asp12 neo-residue in KRAS oncogene.

ADMET Safety Profile: 0.04 hERG cardiac inhibition risk, zero Ames mutagenicity alert, high oral bio-availability.

Resistance Mutation Risk: Low cross-reactivity with HRAS/NRAS wild-types (99.4% oncogene specificity).

💡 Biochemical Note: KRAS was historically labeled "undruggable" for four decades due to its ultra-high picomolar affinity for GTP and lack of deep surface pockets. Machine learning generative algorithms discovered transient cryptic pockets accessible during switch-II oscillation.

⏱️ AI vs. Traditional Oncology R&D Benchmark

Evidence Grounded

Discovery Timeline:

Traditional: ~12.0 Years

In-Silico ML: ~3.5 Years (-71%)

Preclinical & Phase Cost:

Traditional: $2.50 Billion

In-Silico ML: $620 Million (-75%)

Scientific Impact Summary: By replacing blind combinatorial wet-lab screening with transformer-based 3D structure predictions, generative molecular docking, and neural toxicity gates, researchers compress the initial 5-year lead optimization phase down to weeks while avoiding costly late-stage clinical trial attrition.

A saved candidate workbench animates assigned screening counts

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This saved workbench starts with a virtual library count of five hundred thousand. The local funnel assigns two point five percent to pocket fit, producing twelve thousand five hundred. A fixed point zero six eight factor with default affinity coefficient one produces eight hundred fifty binding candidates. At point zero two pixels per count pocket fit twelve thousand five hundred measures two hundred fifty, binding eight hundred fifty seventeen and removed eleven thousand six hundred fifty two hundred thirty three. The bars partition that stage only, not half a million physical molecules. No structure prediction, docking model or chemical database is called. Named algorithms and scientific claims in the original page remain unverified source copy, while this explanation describes actual JavaScript arithmetic. The strict toxicity factor point zero seven five multiplies eight hundred fifty to sixty three point seven five, rounded to sixty four. With the assigned accessibility threshold four point two, its coefficient is point nine. Sixty four times point one times point nine gives five point seven six, rounded to six leads. Relaxing toxicity to low uses point two eight instead and yields two hundred thirty eight before accessibility, then twenty one leads. At one pixel per toxicity count strict sixty four measures sixty four, relaxed two hundred thirty eight two hundred thirty eight and difference one hundred seventy four one hundred seventy four. These changed counts never filter the actual candidate table: the KRAS preset retains its same five stored records and assigned affinity and toxicity labels. The screen animation uses five sixty millisecond ticks, not the displayed randomized thirteen point five to fifteen point five second duration. The pocket drawing always constructs a six atom ring of radius thirty eight canvas pixels. Its diameter is seventy six and circumference about two hundred thirty eight point eight. At one pixel per geometric unit radius measures thirty eight, diameter seventy six and rounded circumference two hundred thirty eight point eight. Changing candidate changes stored labels and colors, not a computed molecular conformation. Changing the custom peptide text saves the text but does not regenerate the three stored neoantigen candidates. Native JSON and CSV exports contain these preset records and assigned benchmark strings. Independent checks must preserve positive canvas drawing buffers through native resize and real downloads. A local animated funnel and ring provide no evidence of binding, safety, clinical success, patient suitability, treatment benefit or actual research acceleration.