‘Binary QSAR Models for ‘Antibiotic-likeness’ and ‘Metabolite-likeness’

GGAGATTCTGGGCCACTTTGGTTCCCCATGAGCCAAGACGGCACTTCTAATTTGCATTCCCTACCGGAGTCCCTGTCTGTAGCCAGCCTGGCTTTCAGCTGGTGCCCAAAGTGACAAATGTATCTGCAATGACAAAGGTACCCTGGAAGGGCTCGCCCTCTGCGGAATTTCAGTTCATGCAGGCCTTGGTGCTTCCACATCTGTCCAAGGGCCTTTCAAATGTGACTTTTAACTCTGTGGATTGATTTGCCCGGTTGTCACATTCTGAGCAGCCACAACCTACTGCATCCCATGTAGAAGTGGAAGTGACCTGATTTTTTCCTGCTTTTCAAGGCTGTATGTTTACATTTGCCTCCAATCATTCCTATGGGAATTCCTTGGGAGTCTAACTTGGAGATTTTGTTTCTTCTGCCTTTGCTCCTGGGGGCTTAATCACTTCTGTGCCTCTGGTTATCTGTGGCACATTTGTATTTGTCATTAGTCAACCGGAGACTCGGGGTCTGAGTGGAGGGTATGTCCCCCTCCAGTGATGGTTTCTGTTGGCTTCCCAGGGTGAGGATGACTCATGACCACTTGCAAGTGGTTTTTGTGTCTGGGGTTTATGATCACACAGTCATACACGTTCTAACTCCAGACTGACTGTTGAGAAAGCCTCTGGGTAAGGGAATTCCTGGGAAACACACTGTTTTCATGCATCCTCTGGAAGATGAGGCCTGAAGTTACCAGGGTCTCTGTTTGCTGATGCTGATGATCCACATTTTCTAGCCCACTCTGCTTCTCTGACACCTTTAGTCTTGAGGATCCATGNTCTGTGAAGGAATCCAAGCTCTCATTTCGCACTCACCTTGGCCCTGGCTCTGTCTCCAGGACCTCTTCTACTACAAAATCCTAAAGCTCTGGGAGCTGGGTGTCAACCTGTGCCCGAGGAAATCATACAGTTACTGTGGACTTTCCAGTTTGCTGTCTTCTAGTATTCCATTGTAGCTCTTGGGTATTTTCCCATCCACCCCAAGATCCAGCTGGAAATCAGTGAACACACTTGATGGGAGTTTTCCTGCATGTGCTCTGGGCATTGACAGTAGAAGGGTGTTCAGAATGTCTGCTGTGCCCTCATGGAGGAAGAGNGCTCAGTGTACATGCTCTGGGTCAGTAGGTGCCCTTGAGCCCAGCTTTGGGAGCAATGTTGGATGAGTGAAGGAGGGATCCAGGGCAAAGCAGGCACGACAGAGTGGAGACGGCGCTGCTGGCTCTCAGGGGAATGGGCATGGAGTGGGTAGGAGATCCACCTAAGGAGGCTGGCTGGCTGGACGAGTCAGGAGCCCCTTCCAAGGGTGGACACTGACAGGCCCCCAGTCTTGGTCTCCTGCATGCCAGAGGTACCAGCCCATCTTTTTTCCTAAACTTGATGACCTAGGGCTAGGGGCATGTTGAATCTCAGCCTCGCCCACTGGCGCTGGACTTGGTACACAGGGTGGGGCAAAGTGGGTACTGGATCCTGATCATCCCTATCCCTGGGGTGTGGCTTCTTGCTGCACAGTCAGCTTCTAGTTCTGTAGCCCCAGCTGCTCCTGCGGTGGAGGGAGCTACACATCAGGCTCTGACCCCCTCCAGGTGGGGCCTTCGCGTGAGGGGAGTCAGCACGCATCAGCAGCTGGGCCCAGGGAGTTGCCCCACTGAGCACTGCGGGCTGACCTGCTCCCAACCAGGGAGATGGAGCTTCCCCCTTGAGTCGGGCTGCTGAAGGGGGGTAGGGGATGGAAACAGTGCGTTTGCAGGAGTAAGGGTGCAGTTGGGTCCCTGCGAGAAAATGTCTCAGTTGTGGCAACTGATTGGTGACCTGGGGGGCGTTTCTGAGCCCACAGTGCTGGCATCAGGACTCAGGTGTGAGGTGCCCCAGACCCTCCCCTTGCCAGTAATTAGCTGATGGCTCGGTGATGCCCAGGGTGAAGGAAGACTTGATTTTGGGAGGGGAGTTCTCTCGTAATGACACTGAGGATGCCTTCAAGTTGGGCTTCTGGCATGTTCTGCCCTCGCTCCCCTTCTGTAGTCACCTTGGCCCTCGTGTTGCTGAGCTGTGTGTGGGAGCGGGAAGCGCGTCAGTGGGCGGAGGGAGCGGGAAGCGCGTCAGTGGGCGGAGTATTTGAGAACATTTCACAAGCCGCTGTTGAGGTTCAGAATCAACCAGCAGATACAGAAACATATTTCGGAGCGTGGGGACCCTTGGGTGAGCTGCCACATGAAGCAGCCCCAGGACCTCCCTGGCTCAAGGAGTGACAGCGAGTTTGTCTGAGGTGAGGGCACAGGCCTGGCGAAGCCTCGTGTGTGGGTGAGACCTGCCCGACCCCAGTGCCTTACCCGAGGAGCTACTGGCCCAGTGGGGGAGGCATTCAGGTGGGCAGAGTCAGGGAGACTCATGAGGCCGTTGAGGCCAGGGGCATAGAGCTGGCCAAGGAGCCATGGCTCACTAACGTGTTGTATGGGGCTCCTTCCCTTCAGGTCCAGGCTCCTGCGTGAAGTGATGCTCCTCTTTGCCTTACTCCTAGCCATGGAGCTCCCATTGGTGGCAGCCAGTGCCACCATCGCGCTCAGTGTAAGTATCATTCCCTCTCACTGTCCTGGAGAGGACGAGAATTCCACCTGGAGATTCTGGGCCACTTTGGTTCCCCATGAGCCAAGACGGCACTTCTAATTTGCATTCCCTACCGGAGTCCCTGTCTGTAGCCAGCCTGGCTTTCAGCTGGTGCCCAAAGTGACAAATGTATCTGCAATGACAAAGGTACCCTGGAAGGGCTCGCCCTCTGCGGAATTTCAGTTCATGCAGGCCTTGGTGCTTCCACATCTGTCCAAGGGCCTTTCAAATGTGACTTTTAACTCTGTGGATTGATTTGCCCGGTTGTCACATTCTGAGCAGCCACAACCTACTGCATCCCATGTAGAAGTGGAAGTGACCTGATTTTTTCCTGCTTTTCAAGGCTGTATGTTTACATTTGCCTCCAATCATTCCTATGGGAATTCCTTGGGAGTCTAACTTGGAGATTTTGTTTCTTCTGCCTTTGCTCCTGGGGGCTTAATCACTTCTGTGCCTCTGGTTATCTGTGGCACATTTGTATTTGTCATTAGTCAACCGGAGACTCGGGGTCTGAGTGGAGGGTATGTCCCCCTCCAGTGATGGTTTCTGTTGGCTTCCCAGGGTGAGGATGACTCATGACCACTTGCAAGTGGTTTTTGTGTCTGGGGTTTATGATCACACAGTCATACACGTTCTAACTCCAGACTGACTGTTGAGAAAGCCTCTGGGTAAGGGAATTCCTGGGAAACACACTGTTTTCATGCATCCTCTGGAAGATGAGGCCTGAAGTTACCAGGGTCTCTGTTTGCTGATGCTGATGATCCACATTTTCTAGCCCACTCTGCTTCTCTGACACCTTTAGTCTTGAGGATCCATGNTCTGTGAAGGAATCCAAGCTCTCATTTCGCACTCACCTTGGCCCTGGCTCTGTCTCCAGGACCTCTTCTACTACAAAATCCTAAAGCTCTGGGAGCTGGGTGTCAACCTGTGCCCGAGGAAATCATACAGTTACTGTGGACTTTCCAGTTTGCTGTCTTCTAGTATTCCATTGTAGCTCTTGGGTATTTTCCCATCCACCCCAAGATCCAGCTGGAAATCAGTGAACACACTTGATGGGAGTTTTCCTGCATGTGCTCTGGGCATTGACAGTAGAAGGGTGTTCAGAATGTCTGCTGTGCCCTCATGGAGGAAGAGNGCTCAGTGTACATGCTCTGGGTCAGTAGGTGCCCTTGAGCCCAGCTTTGGGAGCAATGTTGGATGAGTGAAGGAGGGATCCAGGGCAAAGCAGGCACGACAGAGTGGAGACGGCGCTGCTGGCTCTCAGGGGAATGGGCATGGAGTGGGTAGGAGATCCACCTAAGGAGGCTGGCTGGCTGGACGAGTCAGGAGCCCCTTCCAAGGGTGGACACTGACAGGCCCCCAGTCTTGGTCTCCTGCATGCCAGAGGTACCAGCCCATCTTTTTTCCTAAACTTGATGACCTAGGGCTAGGGGCATGTTGAATCTCAGCCTCGCCCACTGGCGCTGGACTTGGTACACAGGGTGGGGCAAAGTGGGTACTGGATCCTGATCATCCCTATCCCTGGGGTGTGGCTTCTTGCTGCACAGTCAGCTTCTAGTTCTGTAGCCCCAGCTGCTCCTGCGGTGGAGGGAGCTACACATCAGGCTCTGACCCCCTCCAGGTGGGGCCTTCGCGTGAGGGGAGTCAGCACGCATCAGCAGCTGGGCCCAGGGAGTTGCCCCACTGAGCACTGCGGGCTGACCTGCTCCCAACCAGGGAGATGGAGCTTCCCCCTTGAGTCGGGCTGCTGAAGGGGGGTAGGGGATGGAAACAGTGCGTTTGCAGGAGTAAGGGTGCAGTTGGGTCCCTGCGAGAAAATGTCTCAGTTGTGGCAACTGATTGGTGACCTGGGGGGCGTTTCTGAGCCCACAGTGCTGGCATCAGGACTCAGGTGTGAGGTGCCCCAGACCCTCCCCTTGCCAGTAATTAGCTGATGGCTCGGTGATGCCCAGGGTGAAGGAAGACTTGATTTTGGGAGGGGAGTTCTCTCGTAATGACACTGAGGATGCCTTCAAGTTGGGCTTCTGGCATGTTCTGCCCTCGCTCCCCTTCTGTAGTCACCTTGGCCCTCGTGTTGCTGAGCTGTGTGTGGGAGCGGGAAGCGCGTCAGTGGGCGGAGGGAGCGGGAAGCGCGTCAGTGGGCGGAGTATTTGAGAACATTTCACAAGCCGCTGTTGAGGTTCAGAATCAACCAGCAGATACAGAAACATATTTCGGAGCGTGGGGACCCTTGGGTGAGCTGCCACATGAAGCAGCCCCAGGACCTCCCTGGCTCAAGGAGTGACAGCGAGTTTGTCTGAGGTGAGGGCACAGGCCTGGCGAAGCCTCGTGTGTGGGTGAGACCTGCCCGACCCCAGTGCCTTACCCGAGGAGCTACTGGCCCAGTGGGGGAGGCATTCAGGTGGGCAGAGTCAGGGAGACTCATGAGGCCGTTGAGGCCAGGGGCATAGAGCTGGCCAAGGAGCCATGGCTCACTAACGTGTTGTATGGGGCTCCTTCCCTTCAGGTCCAGGCTCCTGCGTGAAGTGATGCTCCTCTTTGCCTTACTCCTAGCCATGGAGCTCCCATTGGTGGCAGCCAGTGCCACCATCGCGCTCAGTGTAAGTATCATTCCCTCTCACTGTCCTGGAGAGGACGAGAATTCCACCTGCCAGTGCCTTACCCGAGGAGCTACTGGCCCAGTGGGGGAGGCATTCAGGTGGGCAGAGTCAGGGAGACTCATGAGGCCGTTGAGGCCAGGGGCATAGAGCTGGCCAAGGAGCCATGGCTCACTAACGTGTTGTATGGGGCTCCTTCCCTTCAGGTCCAGGCTCCTGCGTGAAGTGATGCTCCTCTTTGCCTTACTCCTAGCCATGGAGCTCCCATTGGTGGCAGCCAGTGCCACCATCGCGCTCAGTGTAAGTATCATTCCCTCTCACTGTCCTGGAGAGGACGAGAATTCCACCTGGAGATTCTGGGCCACTTTGGTTCCCCATGAGCCAAGACGGCACTTCTAATTTGCATTCCCTACCGGAGTCCCTGTCTGTAGCCAGCCTGGCTTTCAGCTGGTGCCCAAAGTGACAAATGTATCTGCAATGACAAAGGTACCCTGGAAGGGCTCGCCCTCTGCGGAATTTCAGTTCATGCAGGCCTTGGTGCTTCCACATCTGTCCAAGGGCCTTTCAAATGTGACTTTTAACTCTGTGGATTGATTTGCCCGGTTGTCACATTCTGAGCAGCCACAACCTACTGCATCCCATGTAGAAGTGGAAGTGACCTGATTTTTTCCTGCTTTTCAAGGCTGTATGTTTACATTTGCCTCCAATCATTCCTATGGGAATTCCTTGGGAGTCTAACTTGGAGATTTTGTTTCTTCTGCCTTTGCTCCTGGGGGCTTAATCACTTCTGTGCCTCTGGTTATCTGTGGCACATTTGTATTTGTCATTAGTCAACCGGAGACTCGGGGTCTGAGTGGAGGGTATGTCCCCCTCCAGTGATGGTTTCTGTTGGCTTCCCAGGGTGAGGATGACTCATGACCACTTGCAAGTGGTTTTTGTGTCTGGGGTTTATGATCACACAGTCATACACGTTCTAACTCCAGACTGACTGTTGAGAAAGCCTCTGGGTAAGGGAATTCCTGGGAAACACACTGTTTTCATGCATCCTCTGGAAGATGAGGCCTGAAGTTACCAGGGTCTCTGTTTGCTGATGCTGATGATCCACATTTTCTAGCCCACTCTGCTTCTCTGACACCTTTAGTCTTGAGGATCCATGNTCTGTGAAGGAATCCAAGCTCTCATTTCGCACTCACCTTGGCCCTGGCTCTGTCTCCAGGACCTCTTCTACTACAAAATCCTAAAGCTCTGGGAGCTGGGTGTCAACCTGTGCCCGAGGAAATCATACAGTTACTGTGGACTTTCCAGTTTGCTGTCTTCTAGTATTCCATTGTAGCTCTTGGGTATTTTCCCATCCACCCCAAGATCCAGCTGGAAATCAGTGAACACACTTGATGGGAGTTTTCCTGCATGTGCTCTGGGCATTGACAGTAGAAGGGTGTTCAGAATGTCTGCTGTGCCCTCATGGAGGAAGAGNGCTCAGTGTACATGCTCTGGGTCAGTAGGTGCCCTTGAGCCCAGCTTTGGGAGCAATGTTGGATGAGTGAAGGAGGGATCCAGGGCAAAGCAGGCACGACAGAGTGGAGACGGCGCTGCTGGCTCTCAGGGGAATGGGCATGGAGTGGGTAGGAGATCCACCTAAGGAGGCTGGCTGGCTGGACGAGTCAGGAGCCCCTTCCAAGGGTGGACACTGACAGGCCCCCAGTCTTGGTCTCCTGCATGCCAGAGGTACCAGCCCATCTTTTTTCCTAAACTTGATGACCTAGGGCTAGGGGCATGTTGAAsAACTTGATGACCTAGGGCTAGGGGCATGTTGAAsAAsAAs

T H E U N I V E R S I T Y O F B R I T I S H C O L U M B I A M E D I C I N E I N F E C T I O U S D I S E A S E S



“Emergency discovery” of novel antimicrobials among known drugs in response to new and re-emerging infectious threats

A. CherkasovUBC / VGH Infectious [email protected]





1. ‘Binary QSAR Models for ‘Antibiotic-likeness’ and ‘Metabolite-likeness’

2. Finding potential antibiotics among common drugs, substances 3. Attacking essential proteins in emerging and re-emerging pathogens: Prospectives, Protocols





1. ‘Binary QSAR Models for ‘Antibiotic-likeness’ and ‘Metabolite-likeness’

GGAGATTCTGGGCCACTTTGGTTCCCCATGAGCCAAGACGGCACTTCTAATTTGCATTCCCTACCGGAGTCCCTGTCTGTAGCCAGCCTGGCTTTCAGCTGGTGCCCAAAGTGACAAATGTATCTGCAATGACAAAGGTACCCTGGAAGGGCTCGCCCTCTGCGGAATTTCAGTTCATGCAGGCCTTGGTGCTTCCACATCTGTCCAAGGGCCTTTCAAATGTGACTTTTAACTCTGTGGATTGATTTGCCCGGTTGTCACATTCTGAGCAGCCACAACCTACTGCATCCCATGTAGAAGTGGAAGTGACCTGATTTTTTCCTGCTTTTCAAGGCTGTATGTTTACATTTGCCTCCAATCATTCCTATGGGAATTCCTTGGGAGTCTAACTTGGAGATTTTGTTTCTTCTGCCTTTGCTCCTGGGGGCTTAATCACTTCTGTGCCTCTGGTTATCTGTGGCACATTTGTATTTGTCATTAGTCAACCGGAGACTCGGGGTCTGAGTGGAGGGTATGTCCCCCTCCAGTGATGGTTTCTGTTGGCTTCCCAGGGTGAGGATGACTCATGACCACTTGCAAGTGGTTTTTGTGTCTGGGGTTTATGATCACACAGTCATACACGTTCTAACTCCAGACTGACTGTTGAGAAAGCCTCTGGGTAAGGGAATTCCTGGGAAACACACTGTTTTCATGCATCCTCTGGAAGATGAGGCCTGAAGTTACCAGGGTCTCTGTTTGCTGATGCTGATGATCCACATTTTCTAGCCCACTCTGCTTCTCTGACACCTTTAGTCTTGAGGATCCATGNTCTGTGAAGGAATCCAAGCTCTCATTTCGCACTCACCTTGGCCCTGGCTCTGTCTCCAGGACCTCTTCTACTACAAAATCCTAAAGCTCTGGGAGCTGGGTGTCAACCTGTGCCCGAGGAAATCATACAGTTACTGTGGACTTTCCAGTTTGCTGTCTTCTAGTATTCCATTGTAGCTCTTGGGTATTTTCCCATCCACCCCAAGATCCAGCTGGAAATCAGTGAACACACTTGATGGGAGTTTTCCTGCATGTGCTCTGGGCATTGACAGTAGAAGGGTGTTCAGAATGTCTGCTGTGCCCTCATGGAGGAAGAGNGCTCAGTGTACATGCTCTGGGTCAGTAGGTGCCCTTGAGCCCAGCTTTGGGAGCAATGTTGGATGAGTGAAGGAGGGATCCAGGGCAAAGCAGGCACGACAGAGTGGAGACGGCGCTGCTGGCTCTCAGGGGAATGGGCATGGAGTGGGTAGGAGATCCACCTAAGGAGGCTGGCTGGCTGGACGAGTCAGGAGCCCCTTCCAAGGGTGGACACTGACAGGCCCCCAGTCTTGGTCTCCTGCATGCCAGAGGTACCAGCCCATCTTTTTTCCTAAACTTGATGACCTAGGGCTAGGGGCATGTTGAATCTCAGCCTCGCCCACTGGCGCTGGACTTGGTACACAGGGTGGGGCAAAGTGGGTACTGGATCCTGATCATCCCTATCCCTGGGGTGTGGCTTCTTGCTGCACAGTCAGCTTCTAGTTCTGTAGCCCCAGCTGCTCCTGCGGTGGAGGGAGCTACACATCAGGCTCTGACCCCCTCCAGGTGGGGCCTTCGCGTGAGGGGAGTCAGCACGCATCAGCAGCTGGGCCCAGGGAGTTGCCCCACTGAGCACTGCGGGCTGACCTGCTCCCAACCAGGGAGATGGAGCTTCCCCCTTGAGTCGGGCTGCTGAAGGGGGGTAGGGGATGGAAACAGTGCGTTTGCAGGAGTAAGGGTGCAGTTGGGTCCCTGCGAGAAAATGTCTCAGTTGTGGCAACTGATTGGTGACCTGGGGGGCGTTTCTGAGCCCACAGTGCTGGCATCAGGACTCAGGTGTGAGGTGCCCCAGACCCTCCCCTTGCCAGTAATTAGCTGATGGCTCGGTGATGCCCAGGGTGAAGGAAGACTTGATTTTGGGAGGGGAGTTCTCTCGTAATGACACTGAGGATGCCTTCAAGTTGGGCTTCTGGCATGTTCTGCCCTCGCTCCCCTTCTGTAGTCACCTTGGCCCTCGTGTTGCTGAGCTGTGTGTGGGAGCGGGAAGCGCGTCAGTGGGCGGAGGGAGCGGGAAGCGCGTCAGTGGGCGGAGTATTTGAGAACATTTCACAAGCCGCTGTTGAGGTTCAGAATCAACCAGCAGATACAGAAACATATTTCGGAGCGTGGGGACCCTTGGGTGAGCTGCCACATGAAGCAGCCCCAGGACCTCCCTGGCTCAAGGAGTGACAGCGAGTTTGTCTGAGGTGAGGGCACAGGCCTGGCGAAGCCTCGTGTGTGGGTGAGACCTGCCCGACCCCAGTGCCTTACCCGAGGAGCTACTGGCCCAGTGGGGGAGGCATTCAGGTGGGCAGAGTCAGGGAGACTCATGAGGCCGTTGAGGCCAGGGGCATAGAGCTGGCCAAGGAGCCATGGCTCACTAACGTGTTGTATGGGGCTCCTTCCCTTCAGGTCCAGGCTCCTGCGTGAAGTGATGCTCCTCTTTGCCTTACTCCTAGCCATGGAGCTCCCATTGGTGGCAGCCAGTGCCACCATCGCGCTCAGTGTAAGTATCATTCCCTCTCACTGTCCTGGAGAGGACGAGAATTCCACCTGGAGATTCTGGGCCACTTTGGTTCCCCATGAGCCAAGACGGCACTTCTAATTTGCATTCCCTACCGGAGTCCCTGTCTGTAGCCAGCCTGGCTTTCAGCTGGTGCCCAAAGTGACAAATGTATCTGCAATGACAAAGGTACCCTGGAAGGGCTCGCCCTCTGCGGAATTTCAGTTCATGCAGGCCTTGGTGCTTCCACATCTGTCCAAGGGCCTTTCAAATGTGACTTTTAACTCTGTGGATTGATTTGCCCGGTTGTCACATTCTGAGCAGCCACAACCTACTGCATCCCATGTAGAAGTGGAAGTGACCTGATTTTTTCCTGCTTTTCAAGGCTGTATGTTTACATTTGCCTCCAATCATTCCTATGGGAATTCCTTGGGAGTCTAACTTGGAGATTTTGTTTCTTCTGCCTTTGCTCCTGGGGGCTTAATCACTTCTGTGCCTCTGGTTATCTGTGGCACATTTGTATTTGTCATTAGTCAACCGGAGACTCGGGGTCTGAGTGGAGGGTATGTCCCCCTCCAGTGATGGTTTCTGTTGGCTTCCCAGGGTGAGGATGACTCATGACCACTTGCAAGTGGTTTTTGTGTCTGGGGTTTATGATCACACAGTCATACACGTTCTAACTCCAGACTGACTGTTGAGAAAGCCTCTGGGTAAGGGAATTCCTGGGAAACACACTGTTTTCATGCATCCTCTGGAAGATGAGGCCTGAAGTTACCAGGGTCTCTGTTTGCTGATGCTGATGATCCACATTTTCTAGCCCACTCTGCTTCTCTGACACCTTTAGTCTTGAGGATCCATGNTCTGTGAAGGAATCCAAGCTCTCATTTCGCACTCACCTTGGCCCTGGCTCTGTCTCCAGGACCTCTTCTACTACAAAATCCTAAAGCTCTGGGAGCTGGGTGTCAACCTGTGCCCGAGGAAATCATACAGTTACTGTGGACTTTCCAGTTTGCTGTCTTCTAGTATTCCATTGTAGCTCTTGGGTATTTTCCCATCCACCCCAAGATCCAGCTGGAAATCAGTGAACACACTTGATGGGAGTTTTCCTGCATGTGCTCTGGGCATTGACAGTAGAAGGGTGTTCAGAATGTCTGCTGTGCCCTCATGGAGGAAGAGNGCTCAGTGTACATGCTCTGGGTCAGTAGGTGCCCTTGAGCCCAGCTTTGGGAGCAATGTTGGATGAGTGAAGGAGGGATCCAGGGCAAAGCAGGCACGACAGAGTGGAGACGGCGCTGCTGGCTCTCAGGGGAATGGGCATGGAGTGGGTAGGAGATCCACCTAAGGAGGCTGGCTGGCTGGACGAGTCAGGAGCCCCTTCCAAGGGTGGACACTGACAGGCCCCCAGTCTTGGTCTCCTGCATGCCAGAGGTACCAGCCCATCTTTTTTCCTAAACTTGATGACCTAGGGCTAGGGGCATGTTGAATCTCAGCCTCGCCCACTGGCGCTGGACTTGGTACACAGGGTGGGGCAAAGTGGGTACTGGATCCTGATCATCCCTATCCCTGGGGTGTGGCTTCTTGCTGCACAGTCAGCTTCTAGTTCTGTAGCCCCAGCTGCTCCTGCGGTGGAGGGAGCTACACATCAGGCTCTGACCCCCTCCAGGTGGGGCCTTCGCGTGAGGGGAGTCAGCACGCATCAGCAGCTGGGCCCAGGGAGTTGCCCCACTGAGCACTGCGGGCTGACCTGCTCCCAACCAGGGAGATGGAGCTTCCCCCTTGAGTCGGGCTGCTGAAGGGGGGTAGGGGATGGAAACAGTGCGTTTGCAGGAGTAAGGGTGCAGTTGGGTCCCTGCGAGAAAATGTCTCAGTTGTGGCAACTGATTGGTGACCTGGGGGGCGTTTCTGAGCCCACAGTGCTGGCATCAGGACTCAGGTGTGAGGTGCCCCAGACCCTCCCCTTGCCAGTAATTAGCTGATGGCTCGGTGATGCCCAGGGTGAAGGAAGACTTGATTTTGGGAGGGGAGTTCTCTCGTAATGACACTGAGGATGCCTTCAAGTTGGGCTTCTGGCATGTTCTGCCCTCGCTCCCCTTCTGTAGTCACCTTGGCCCTCGTGTTGCTGAGCTGTGTGTGGGAGCGGGAAGCGCGTCAGTGGGCGGAGGGAGCGGGAAGCGCGTCAGTGGGCGGAGTATTTGAGAACATTTCACAAGCCGCTGTTGAGGTTCAGAATCAACCAGCAGATACAGAAACATATTTCGGAGCGTGGGGACCCTTGGGTGAGCTGCCACATGAAGCAGCCCCAGGACCTCCCTGGCTCAAGGAGTGACAGCGAGTTTGTCTGAGGTGAGGGCACAGGCCTGGCGAAGCCTCGTGTGTGGGTGAGACCTGCCCGACCCCAGTGCCTTACCCGAGGAGCTACTGGCCCAGTGGGGGAGGCATTCAGGTGGGCAGAGTCAGGGAGACTCATGAGGCCGTTGAGGCCAGGGGCATAGAGCTGGCCAAGGAGCCATGGCTCACTAACGTGTTGTATGGGGCTCCTTCCCTTCAGGTCCAGGCTCCTGCGTGAAGTGATGCTCCTCTTTGCCTTACTCCTAGCCATGGAGCTCCCATTGGTGGCAGCCAGTGCCACCATCGCGCTCAGTGTAAGTATCATTCCCTCTCACTGTCCTGGAGAGGACGAGAATTCCACCTGCCAGTGCCTTACCCGAGGAGCTACTGGCCCAGTGGGGGAGGCATTCAGGTGGGCAGAGTCAGGGAGACTCATGAGGCCGTTGAGGCCAGGGGCATAGAGCTGGCCAAGGAGCCATGGCTCACTAACGTGTTGTATGGGGCTCCTTCCCTTCAGGTCCAGGCTCCTGCGTGAAGTGATGCTCCTCTTTGCCTTACTCCTAGCCATGGAGCTCCCATTGGTGGCAGCCAGTGCCACCATCGCGCTCAGTGTAAGTATCATTCCCTCTCACTGTCCTGGAGAGGACGAGAATTCCACCTGGAGATTCTGGGCCACTTTGGTTCCCCATGAGCCAAGACGGCACTTCTAATTTGCATTCCCTACCGGAGTCCCTGTCTGTAGCCAGCCTGGCTTTCAGCTGGTGCCCAAAGTGACAAATGTATCTGCAATGACAAAGGTACCCTGGAAGGGCTCGCCCTCTGCGGAATTTCAGTTCATGCAGGCCTTGGTGCTTCCACATCTGTCCAAGGGCCTTTCAAATGTGACTTTTAACTCTGTGGATTGATTTGCCCGGTTGTCACATTCTGAGCAGCCACAACCTACTGCATCCCATGTAGAAGTGGAAGTGACCTGATTTTTTCCTGCTTTTCAAGGCTGTATGTTTACATTTGCCTCCAATCATTCCTATGGGAATTCCTTGGGAGTCTAACTTGGAGATTTTGTTTCTTCTGCCTTTGCTCCTGGGGGCTTAATCACTTCTGTGCCTCTGGTTATCTGTGGCACATTTGTATTTGTCATTAGTCAACCGGAGACTCGGGGTCTGAGTGGAGGGTATGTCCCCCTCCAGTGATGGTTTCTGTTGGCTTCCCAGGGTGAGGATGACTCATGACCACTTGCAAGTGGTTTTTGTGTCTGGGGTTTATGATCACACAGTCATACACGTTCTAACTCCAGACTGACTGTTGAGAAAGCCTCTGGGTAAGGGAATTCCTGGGAAACACACTGTTTTCATGCATCCTCTGGAAGATGAGGCCTGAAGTTACCAGGGTCTCTGTTTGCTGATGCTGATGATCCACATTTTCTAGCCCACTCTGCTTCTCTGACACCTTTAGTCTTGAGGATCCATGNTCTGTGAAGGAATCCAAGCTCTCATTTCGCACTCACCTTGGCCCTGGCTCTGTCTCCAGGACCTCTTCTACTACAAAATCCTAAAGCTCTGGGAGCTGGGTGTCAACCTGTGCCCGAGGAAATCATACAGTTACTGTGGACTTTCCAGTTTGCTGTCTTCTAGTATTCCATTGTAGCTCTTGGGTATTTTCCCATCCACCCCAAGATCCAGCTGGAAATCAGTGAACACACTTGATGGGAGTTTTCCTGCATGTGCTCTGGGCATTGACAGTAGAAGGGTGTTCAGAATGTCTGCTGTGCCCTCATGGAGGAAGAGNGCTCAGTGTACATGCTCTGGGTCAGTAGGTGCCCTTGAGCCCAGCTTTGGGAGCAATGTTGGATGAGTGAAGGAGGGATCCAGGGCAAAGCAGGCACGACAGAGTGGAGACGGCGCTGCTGGCTCTCAGGGGAATGGGCATGGAGTGGGTAGGAGATCCACCTAAGGAGGCTGGCTGGCTGGACGAGTCAGGAGCCCCTTCCAAGGGTGGACACTGACAGGCCCCCAGTCTTGGTCTCCTGCATGCCAGAGGTACCAGCCCATCTTTTTTCCTAAACTTGATGACCTAGGGCTAGGGGCATGTTGAA

QSARquantitative structure-activity relationships

Molecular structuregets translated into numbers

(descriptors)

THE UNIVERSITY OF BRITISH COLUMBIA MEDICINE INFECTIOUS DISEASES

Mathematical function

f ( Descriptors) ~ Activity


Inductive descriptors

n

jiGi ji

ijG r

RRs

,2

2

n

jiGi ji

ijijG r

R

,2

200* )(

1

2

22

1

2

220

01

)(

N

ji ji

ji

N

ji ji

jii

jN

r

RR

r

RR

2

2

22

22

AB

B

ABA

BA

atom

shadow

r

R

rR

RR

S

S

The relative area of frontal Screening of a surface of atom A by atom B (shown as a green shadow) is





“Inductive ” QSAR Descriptors

Average Electronegativities of the Negatively/Positively Charged AtomsMolecular (equalized) ElectronegativityTotal Formal ChargeAverage Atomic HardnessSum of Atomic HardnessesAverage Negative/Positive ChargesLargest Positive ChargeAverage Atomic Hardnesses of Negatively/Positively Charged AtomsHardness of the Most Positively Charged AtomLargest Hardness among the Negatively/Positively Charged AtomsSum of Softnesses of Negatively Charged AtomsSteric Effect on the Most Negatively Charged AtomMost Negative Inductive Constant of an Atom in MoleculeLargest Positive Inductive Effect on an Atom in MoleculeThe Smallest Steric Effect on a Atom in MoleculeSum of all Negative Inductive Effects on Atoms in Molecule …..





QSAR models for antibiotics, drugs and bacteria and human metabolitesA. Cherkasov. J Chem Inf Model, 46, 1214 (2006), E. Karakoc et al Bioinformatics, 22, e243v (2006)QSAR models for antimicrobial peptides A. Cherkasov. Current Computer-Aided Drug Design, 1, 21 (2005)H. Jenssen et al Chem Biol Drug Des. 70, 134 (2007)A hybrid QSAR/Docking approach ‘Progressive docking’ has been developedA. Cherkasov et al, J Med Chem. 49, 7466 (2006)Various low-micromolar to nanomolar inhibitors developed with the help of ‘inductive’ QSAR solutionsA. Cherkasov et al, J Med Chem, 48, 3203 (2005)A. Cherkasov et al J Chem Inf Model, 45, 1842 (2005)QSAR model for pK constants of carboxylic acids and protonated aminesA. Cherkasov et al, J Phys Chem, 107, 9695 (2003)Descriptors-derived system for protein charge calculationA. Cherkasov. J Chem Inf Comp Sci, 43, 2039 (2003)QSAR model for dipole momentsA. Cherkasov et al, J. Mol. Struct. Theochem, 497, 115 (2000)QSAR models for oxidation energies of free radicals A Cherkasov et al, J Chem Inf Comp Sci, 40, 1222 (2000), 39, 1057 (1999), 38, 1151 (1998)QSAR models for Gas Basicity of AminesA. Cherkasov et al, J Mol Graph Model, 17, 28 (1999)Predictive model for inductive substituent constants. A. Cherkasov et al, J Phys Org Chem 11, 437 (1998)





QSAR MODEL FOR ANTIBIOTICS

A mixture of known antibiotics and non-antibiotics

Table 1Testing peptide classification performance. QSAR - predictions based on QSAR descriptors; AA - predictions based on amino-acid sequence only.

2/3 of all compounds for training 1/3 of all compounds for testing

VALIDATION: How the model performs on test compounds

QSAR model for the training set





0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30QSAR Descriptors

Mea

n no

rmal

ized

AntimicrobialBacterial MetabolitesHuman MetabolitesDrugsDruglikes



A number of QSAR models have been elaborated to separate individual clusters within the dataset of 958 human therapeutics, 519 antimicrobials, 1202 drug-like chemicals, as well as 1102 human-, 551 bacterial-, 2351 plant- and 825 fungal metabolites.

Antimicrobials from Drugs

Antimicrobials from Drug-likes

Distinguishing Antimicrobials from all others

Distinguishing Antimicrobials versus Drugs versus Drug-likes

QSAR model for Bacterial Metabolites

Train Test Train Test Train Test Train Test Train Test

T_P 327 130 332 140 294 124 270 89 360 139

T_N 631 248 841 342 1490 621 1486 644 792 347

F_P 49 33 7 14 32 20 17 14 39 26

F_N 33 35 30 23 66 41 108 58 48 19

SPEC 0.93 0.88 0.99 0.96 0.98 0.97 0.99 0.98 0.95 0.93

SENS 0.91 0.79 0.92 0.86 0.82 0.75 0.71 0.61 0.88 0.88

ACCUR

0.92 0.85 0.97 0.93 0.95 0.92 0.93 0.91 0.93 0.92

PPV 0.87 0.80 0.98 0.91 0.90 0.86 0.94 0.86 0.90 0.84

NPV 0.95 0.88 0.97 0.94 0.96 0.94 0.93 0.92 0.94 0.95


Method ValidationTruePosit.

TrueNegat FalsePosit.FalseNegat

Spec. Sens. Accur PPV NPV

Antibacterials versus (Drugs + Drug-likes + Bacteria Metabolites + Human Metabolites)

kNN

Training 70% 269 2610 9 95 0.97 0.74 0.95 0.80 0.96

Testing 30% 117 1119 8 39 0.98 0.75 0.95 0.81 0.97

LOO 400 3727 9 120 0.97 0.77 0.95 0.80 0.97

LDA

Training 70% 364 0 2679 0 0.00 1.00 0.12 0.12 0.00

Testing 30% 156 0 1147 0 0.00 1.00 0.12 0.12 0.00

LOO 261 3751 75 259 0.98 0.50 0.92 0.78 0.94

MLR

Training 70% 194 564 2115 170 0.21 0.53 0.25 0.08 0.77

Testing 30% 61 1129 18 95 0.98 0.39 0.91 0.77 0.92

LOO 279 3726 100 241 0.97 0.54 0.92 0.74 0.94

ANN

Training 70% 294 2651 27 70 0.99 0.81 0.97 0.92 0.97

Testing 30% 129 1132 16 27 0.99 0.83 0.97 0.89 0.98

LOO 449 3821 5 71 0.99 0.86 0.98 0.99 0.98

Bacteria Metabolites versus (Drugs + Drug-likes + Antibacterials + Human Metabolites)

kNN

Training 70% 311 2537 112 83 0.96 0.79 0.94 074 0.97

Testing 30% 135 1091 44 33 0.96 0.80 0.94 0.75 0.97

LOO 455 3637 147 107 0.96 0.81 0.94 0.76 0.97

LDA

Training 70% 240 2587 62 154 0.98 0.61 0.93 0.79 0.94

Testing 30% 90 1088 47 78 0.96 0.54 0.90 0.66 0.93

LOO 336 3665 119 226 0.97 0.60 0.92 0.74 0.94

MLR

Training 70% 301 2525 124 93 0.95 0.76 0.93 0.71 0.96

Testing 30% 119 1073 62 49 0.95 0.71 0.91 0.66 0.96

LOO 406 3603 181 156 0.95 0.72 0.92 0.69 0.96

ANN

Training 70% 338 2597 52 55 0.98 0.86 0.96 0.87 0.98

Testing 30% 159 1076 59 10 0.95 0.94 0.95 0.73 0.99

LOO 534 3780 4 28 0.99 0.95 0.99 0.99 0.99



Retreival of Human Methabolites

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Query number

Ret

riva

l Per

cent

age

R=0.1

R=0.15

R=0.2

R=0.1 Random

R=0.15 Random

R=0.2 Random

Retrieval of Antibiotic Compounds

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Ret

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R=0.15 Random

R=0.20 Random





Separation of various classes of substances in the chemical space

Antibacterials

Inactive Chemicals

General drugs

Bacterial metabolites





2. Finding potential antibiotics among common drugs, substances





Bad Bugs Need Drugs: IDSA, March 2006

Antimicrobial Availability Task Force Widespread prevalence of MDR bacteria in hospitals Few drugs in a pipeline, Urgent need for R&D

Experts Fear Increase in Drug-resistant Infectious Here: Globe and Mail, March 2006

MRSA, a treatment-resistant form of bacteria that spreads through direct contact, is called a greater threat to public health than SARS or bird flu. The Boston Globe, August 21, 2006





Resistance mechanisms are very effective (specific toward the existing antibiotics)






Lateral gene transfer…







Active transport…







Active transport…

Resistant mutations…






BUT ‘off-target’ bacterial inhibition by non-antibiotics might still work…


Active transport…







BUT ‘off-target’ bacterial inhibition by non-antibiotics might still work… NEED TO FIND SUCH AB-LIKE DRUGS


Active transport…




Lead compound Merck annotation

BML score

Antibiotic-likeness (30-10-2 ANN)

Metabolite analogue

O

O

OH

OO

Lovastatin

Antilipemic 0.71 0.56 O

O

OH

OO

NP-007587

O

OO

O

O

OO

O

OH

OH OH

O

O

O

OH

O

O

OH

O

O

OO

OH

Olivomycin A

Antineoplastic Cytostatic agent

0.72 0.99

O

OO

O

OO

OH

OH

OH OH

O

O

OH

O

O

OH

O

O

OOOH

O

O

OH

OH

NP-009248

O

OHOH

OH Gentisic acid

Analgesic; Antiinflamatory

0.74 0.66

O

OH

OH

OH

NP-001423

MERCK Database scaningMERCK Database scaning



Lead compound Merck annotation

BML score

Antibiotic-likeness (30-10-2 ANN)

Metabolite analogue

O

O

OH

OO

Lovastatin

Antilipemic 0.71 0.56 O

O

OH

OO

NP-007587

O

OO

O

O

OO

O

OH

OH OH

O

O

O

OH

O

O

OH

O

O

OO

OH

Olivomycin A

Antineoplastic Cytostatic agent

0.72 0.99

O

OO

O

OO

OH

OH

OH OH

O

O

OH

O

O

OH

O

O

OOOH

O

O

OH

OH

NP-009248

O

OHOH

OH Gentisic acid

Analgesic; Antiinflamatory

0.74 0.66

O

OH

OH

OH

NP-001423

MERCK Database scaningMERCK Database scaning

CONFIRMEDCONFIRMED

CONFIRMEDCONFIRMED

CONFIRMEDCONFIRMED





3. Attacking essential proteins in emerging and re-emerging pathogens: Prospectives, Protocols

IN SILICO

POOL OF NOVEL DRUG TARGETS

EXPERIMENT

Protein-protein interaction data(MASS SPEC)

Bacterial genome

1000 bate proteins from Salmonella and S. aureus MRSA to be

expressed and tagged

Pullout experiment

Reconstruction of protein-protein

interaction network (experimenatal +

tentative)

Human genome

Bioinformatics Target

Identification, Aims 1 and 2

“Hubs” of bacterial protein-protein interaction networks

INDEL containing essential proteins

‘Mimickers’ proteins

Known antimicrobial compounds

“ANTIBIOTIC-LIKE” RANKING OF

ELECTRONIC COLLECTIONS OF UNTESTED COMPOUNDS

“Creation of antibiotic-likeness”

modelsAim 3

TARGET-BASEDDRUG DESIGN


PINs

Jeong, H., Mason, S. P., Barabasi, A. L., & Oltvai, Z. N. (2001). Lethality and centrality in protein networks. Nature, 411, 41-42.

In most protein interaction networks (PINs), there the majority of proteins In most protein interaction networks (PINs), there the majority of proteins participate in few interactions while the vast number of interactions occur participate in few interactions while the vast number of interactions occur through few essential ‘HUB’ proteins.through few essential ‘HUB’ proteins.

PINs are resistant to PINs are resistant to random attack, but very random attack, but very vulnerable when it comes vulnerable when it comes to ‘intelligent’ removal of to ‘intelligent’ removal of the hubsthe hubs

IN SILICO

POOL OF NOVEL DRUG TARGETS

EXPERIMENT

Protein-protein interaction data(MASS SPEC)

Bacterial genome

1000 bate proteins from Salmonella and S. aureus MRSA to be

expressed and tagged

Pullout experiment

Reconstruction of protein-protein

interaction network (experimenatal +

tentative)

Human genome

Bioinformatics Target

Identification, Aims 1 and 2

“Hubs” of bacterial protein-protein interaction networks

INDEL containing essential proteins

‘Mimickers’ proteins

Known antimicrobial compounds

“ANTIBIOTIC-LIKE” RANKING OF

ELECTRONIC COLLECTIONS OF UNTESTED COMPOUNDS

“Creation of antibiotic-likeness”

modelsAim 3

TARGET-BASEDDRUG DESIGN





Canadian Institute of Health Research (CIHR)

UBC/VHHSC

Genome Canada; Genome BC

Acknowledgements:





“Emergency discovery” of novel antimicrobials among known drugs in response to new and re-emerging infectious threats

A. CherkasovUBC / VGH Infectious [email protected]





Approved DRUGS re-profiled into:

“FIRST-LINE DEFENCE” Antibiotics

Tropical- and other 3rd world Anti-infectives

Orphan therapeutics

‘Binary QSAR Models for ‘Antibiotic-likeness’ and ‘Metabolite-likeness’

Documents

Transcript of ‘Binary QSAR Models for ‘Antibiotic-likeness’ and ‘Metabolite-likeness’