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| 1 | +# ============================================================================== |
| 2 | +# AETHER Scientific Computing Showcase: |
| 3 | +# Computational Biology, Genomics & AI Drug Discovery (Medicine Prediction) |
| 4 | +# ============================================================================== |
| 5 | + |
| 6 | +print("==================================================================") |
| 7 | +print(" AETHER NATIVE GENOMICS & COMPUTATIONAL DRUG DISCOVERY ENGINE ") |
| 8 | +print("==================================================================") |
| 9 | + |
| 10 | +# ------------------------------------------------------------------------------ |
| 11 | +# 1. 2-Bit Compressed DNA & Sequence Processing |
| 12 | +# ------------------------------------------------------------------------------ |
| 13 | +print("\n--- 1. Native 2-Bit DNA Compression & Genetics ---") |
| 14 | + |
| 15 | +let raw_dna = "ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG" |
| 16 | +let dna = Bio.dna(raw_dna) |
| 17 | + |
| 18 | +print("Original DNA Sequence:", dna.to_string()) |
| 19 | +print("Sequence Length (bp):", dna.len) |
| 20 | +print("Packed Memory (bytes):", dna.raw_bytes) |
| 21 | +print("Memory Compression Ratio:", dna.compression_ratio, "x (75% RAM savings)") |
| 22 | +print("GC-Content (%):", dna.gc_content()) |
| 23 | + |
| 24 | +let rev_comp = dna.reverse_complement() |
| 25 | +print("Reverse Complement:", rev_comp.to_string()) |
| 26 | + |
| 27 | +let mrna = dna.transcribe() |
| 28 | +print("Transcribed mRNA:", mrna) |
| 29 | + |
| 30 | +let protein = dna.translate(0) |
| 31 | +print("Translated Amino Acid Chain (Frame 0):", protein) |
| 32 | + |
| 33 | +# ------------------------------------------------------------------------------ |
| 34 | +# 2. Gene Discovery: Open Reading Frames (ORFs) & K-Mer Profiling |
| 35 | +# ------------------------------------------------------------------------------ |
| 36 | +print("\n--- 2. Gene Discovery: ORFs & K-Mer Profiling ---") |
| 37 | + |
| 38 | +let orfs = dna.find_orfs(2) |
| 39 | +print("Detected Open Reading Frames (ORFs):", orfs.len()) |
| 40 | +for orf in orfs { |
| 41 | + print(" -> Found ORF at [", orf.start, ":", orf.end, "] | Codons:", orf.codons, "| Protein:", orf.protein) |
| 42 | +} |
| 43 | + |
| 44 | +let kmer_counts = dna.count_kmers(3) |
| 45 | +print("Top 3-mers sample: ATG ->", kmer_counts["ATG"], "| GCC ->", kmer_counts["GCC"]) |
| 46 | + |
| 47 | +# ------------------------------------------------------------------------------ |
| 48 | +# 3. Global Sequence Alignment & SNP Mutation Detection |
| 49 | +# ------------------------------------------------------------------------------ |
| 50 | +print("\n--- 3. Sequence Alignment (Needleman-Wunsch) & SNP Detection ---") |
| 51 | + |
| 52 | +let wild_type = Bio.dna("ATGGTGCATCTGACTCCT") |
| 53 | +let mutant_variant = Bio.dna("ATGGTGCACCTGACTCCT") |
| 54 | + |
| 55 | +let snp_mutations = wild_type.hamming_distance(mutant_variant) |
| 56 | +print("Detected Point Mutations (SNPs):", snp_mutations) |
| 57 | + |
| 58 | +let alignment = Bio.align(wild_type.to_string(), mutant_variant.to_string(), 2, -1, -2) |
| 59 | +print("Alignment Score:", alignment.score) |
| 60 | +print("Aligned Ref: ", alignment.aligned1) |
| 61 | +print("Aligned Query:", alignment.aligned2) |
| 62 | +print("Sequence Identity (%):", alignment.identity_percent) |
| 63 | + |
| 64 | +# ------------------------------------------------------------------------------ |
| 65 | +# 4. Computational Chemistry: SMILES Molecular Parsing & Physicochemical Rules |
| 66 | +# ------------------------------------------------------------------------------ |
| 67 | +print("\n--- 4. Computational Chemistry & Drug Candidates ---") |
| 68 | + |
| 69 | +let aspirin = Chem.from_smiles("CC(=O)OC1=CC=CC=C1C(=O)O") |
| 70 | +let ibuprofen = Chem.from_smiles("CC(C)CC1=CC=C(C=C1)C(C)C(=O)O") |
| 71 | +let paracetamol = Chem.from_smiles("CC(=O)NC1=CC=C(O)C=C1") |
| 72 | +let caffeine = Chem.from_smiles("CN1C=NC2=C1C(=O)N(C(=O)N2C)C") |
| 73 | + |
| 74 | +let candidates = [ |
| 75 | + ("Aspirin", aspirin), |
| 76 | + ("Ibuprofen", ibuprofen), |
| 77 | + ("Paracetamol", paracetamol), |
| 78 | + ("Caffeine", caffeine) |
| 79 | +] |
| 80 | + |
| 81 | +for item in candidates { |
| 82 | + let name = item[0] |
| 83 | + let mol = item[1] |
| 84 | + let r5 = mol.rule_of_five() |
| 85 | + print("Drug Candidate:", name) |
| 86 | + print(" Formula:", mol.formula(), "| MW:", mol.weight(), "Da | Heavy Atoms:", mol.heavy_atoms) |
| 87 | + print(" LogP (Lipophilicity):", mol.logp(), "| TPSA:", mol.tpsa(), "Ų") |
| 88 | + print(" H-Bond Donors:", mol.hbd(), "| Acceptors:", mol.hba(), "| Rotatable Bonds:", mol.rotatable_bonds()) |
| 89 | + print(" Lipinski Rule of 5 Oral Bioavailability:", r5.is_drug_like, "(Violations:", r5.violations, ")") |
| 90 | +} |
| 91 | + |
| 92 | +# ------------------------------------------------------------------------------ |
| 93 | +# 5. Extended Connectivity Fingerprints (ECFP) & Chemical Similarity |
| 94 | +# ------------------------------------------------------------------------------ |
| 95 | +print("\n--- 5. Molecular Fingerprinting & Tanimoto Structural Similarity ---") |
| 96 | + |
| 97 | +let fp_asp = aspirin.fingerprint(512) |
| 98 | +let fp_ibu = ibuprofen.fingerprint(512) |
| 99 | +let fp_par = paracetamol.fingerprint(512) |
| 100 | +let fp_caf = caffeine.fingerprint(512) |
| 101 | + |
| 102 | +let sim_asp_par = Chem.tanimoto_similarity(fp_asp, fp_par) |
| 103 | +let sim_asp_ibu = Chem.tanimoto_similarity(fp_asp, fp_ibu) |
| 104 | +let sim_asp_caf = Chem.tanimoto_similarity(fp_asp, fp_caf) |
| 105 | + |
| 106 | +print("Aspirin vs Paracetamol Structural Similarity:", sim_asp_par) |
| 107 | +print("Aspirin vs Ibuprofen Structural Similarity:", sim_asp_ibu) |
| 108 | +print("Aspirin vs Caffeine Structural Similarity: ", sim_asp_caf) |
| 109 | + |
| 110 | +# ------------------------------------------------------------------------------ |
| 111 | +# 6. AI Virtual Screening & Receptor Target Binding Affinity Prediction |
| 112 | +# ------------------------------------------------------------------------------ |
| 113 | +print("\n--- 6. AI Medicine Prediction: Receptor Target Binding Affinity ---") |
| 114 | + |
| 115 | +# Target Receptor Pocket Features (e.g. COX-2 enzyme active site model) |
| 116 | +let mut cox2_weights = [] |
| 117 | +for i in 0..512 { |
| 118 | + if i % 12 == 0 { |
| 119 | + cox2_weights.push(0.42) |
| 120 | + } else if i % 7 == 0 { |
| 121 | + cox2_weights.push(0.18) |
| 122 | + } else { |
| 123 | + cox2_weights.push(0.02) |
| 124 | + } |
| 125 | +} |
| 126 | + |
| 127 | +for item in candidates { |
| 128 | + let name = item[0] |
| 129 | + let mol = item[1] |
| 130 | + let affinity = Chem.predict_binding_affinity(mol, cox2_weights, 4.8) |
| 131 | + print("Molecule:", name) |
| 132 | + print(" Predicted pIC50:", affinity.pic50) |
| 133 | + print(" Estimated Dissociation Constant (Kd):", affinity.kd_nm, "nM") |
| 134 | + print(" Binding Classification:", affinity.binding_class) |
| 135 | +} |
| 136 | + |
| 137 | +# ------------------------------------------------------------------------------ |
| 138 | +# 7. Native Language Unit Test |
| 139 | +# ------------------------------------------------------------------------------ |
| 140 | +test "scientific verification" { |
| 141 | + let asp = Chem.from_smiles("CC(=O)OC1=CC=CC=C1C(=O)O") |
| 142 | + let asp_r5 = asp.rule_of_five() |
| 143 | + assert_eq(asp_r5.is_drug_like, true) |
| 144 | + assert_eq(asp.formula(), "C9H8O4") |
| 145 | + |
| 146 | + let test_dna = Bio.dna("ATGGCCATTTAA") |
| 147 | + assert_eq(test_dna.translate(0), "MAI*") |
| 148 | +} |
| 149 | + |
| 150 | +print("\nAll scientific simulations and analyses completed successfully!") |
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