SYBYL Base
: Complete Computational Chemistry and Molecular Modeling Environment
Overview
SYBYL®Àº Tripos°¡ Á¦°øÇÏ´Â Àü¹®ÀûÀÎ ºÐÀÚ ¸ðµ¨¸µ ȯ°æÀ¸·Î, »õ·Î¿î ÈÇÕ¹° ±¸Á¶ÀÇ ¼³°è¿¡ ÃÊÁ¡À» µÎ°í ºÐÀÚ ±¸Á¶¿Í Ư¼ºÀ» ÀÌÇØÇϴµ¥ ÇÊ¿äÇÑ ±âº»ÀûÀÎ µµ±¸µéÀ» Á¦°øÇÕ´Ï´Ù. SYBYLÀÇ ±â´ÉµéÀº small ligand³ª macro molecule ¸ðµÎ¿¡ Àû¿ë °¡´ÉÇÏ¸ç ±¸Á¶ ¼³°è, ÆíÁý, ½Ã°¢È µµ±¸»Ó¸¸ ¾Æ´Ï¶ó moleculeÀÇ ÈÇÐÀû Á¤º¸¿Í Ç¥ÁØ µ¥ÀÌÅÍ Á¶ÀÛ µµ±¸¸¦ °áÇÕÇÑ µ¥ÀÌÅÍ ÆíÁý ¹× ºÐ¼® µµ±¸µµ Á¦°øÇϰí ÀÖ½À´Ï´Ù. ¶ÇÇÑ SYBYLÀÇ ÇÁ·Î±×·¡¹Ö ¾ð¾î¿Í °³¹æÇü ¾ÆÅ°ÅØÃ³´Â »ç¿ëÀÚÀÇ È¯°æ°ú »óȲ¿¡ ¸Â´Â ¿¬±¸°¡ °¡´ÉÇϵµ·Ï µµ¿ï °Í ÀÔ´Ï´Ù.
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An enzyme mimic, this small organomanganese complex duplicates the catalytic activity of super oxide dismutase, a 31KD protein. The complex is rendered in ball-and-stick mode and shown with an isopotential contour.
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Key Benefits
· Building and Editing .
· Computation
· Analysis and Organization
· Visualization
· Customization

MOLCAD
: Advanced Visualization of Molecular Surfaces and Properties
Overview
MOLCAD™´Â ±×·¡ÇÈ À̹ÌÁö¸¦ ¸¸µé¾î ºÐÀÚ¸¦ ÀνÄÇϴµ¥ ÇʼöÀûÀΠƯ¼ºµéÀ» ½Ã°¢ÈÇÏ´Â ÇÁ·Î±×·¥ÀÔ´Ï´Ù. Van der Waals¿Í solvent-accessible surfaceÀ» °è»êÇÒ ¼ö ÀÖÀ¸¸ç, »ý¼ºµÈ surface¿¡ lipophilic potential, electrostatic potential, ¼ö¼Ò °áÇÕ ´É·Â, ºÎºÐ °î·ü, °Å¸® µîÀÇ ´Ù¾çÇÑ Æ¯¼ºµéÀ» ¸ÊÇÎÇÏ¿© º¸¿©ÁÙ ¼ö ÀÖ½À´Ï´Ù.
MOLCAD¸¦ ÀÌ¿ëÇϸé ÀÛÀº ºÐÀÚ»Ó¸¸ ¾Æ´Ï¶ó ´Ü¹éÁú ³»ÀÇ Ã¤³ÎÀ̳ª °øµ¿(cavity)ÀÇ surface¸¦ °è»êÇÏ¿© º¸¿©ÁÙ ¼ö ÀÖ½À´Ï´Ù. ¶ÇÇÑ ´Ü¹éÁúÀÇ ÀÌÂ÷, »ïÂ÷ ±¸Á¶¸¦ ³ªÅ¸³»´Â ¸®º» ÇüŸ¦ »ý¼ºÇÑ ÈÄ residue lipophilicity, atomic temperature factor¿¡ ±Ù°ÅÇÑ flexibility, packing density¿Í °°Àº ¹°¸®ÀûÀΠƯ¼ºµéÀ» ¸ÊÇÎÇÏ¿© º¸¿©ÁÙ ¼ö ÀÖÀ¸¸ç AMPAC·Î »ý¼ºµÈ surface¸¦ ÀüÀÚ ¹Ðµµ, electrostatic potential, ºÐÀÚ ±Ëµµ ÇÔ¼ö µîÀ» º¸¿©ÁÖ´Â ±â´ÉÀ» Á¦°øÇÕ´Ï´Ù.
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Methotrexate bound to dihydrofolate reductase. A MOLCAD-generated surface has been created for methotrexate and is color-coded by electrostatic potential. The inhibitor, methotrexate, is rendered as capped sticks, while the protein is shown as lines.  
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Dynamics
: Easily Investigate and Visualize Molecular Structure and Mobility
Overview
Molecular dynamics´Â ºÐÀÚÀÇ ¿òÁ÷ÀÓÀ» ½Ã¹Ä·¹À̼ÇÇϰí conformation °ø°£À» Ž»öÇÏ¿© ºÐÀÚÀÇ ±¸Á¶ ¹× À̵¿¼º¿¡ ´ëÇÑ ÅëÂûÀ» °¡´ÉÇÏ°Ô ÇÕ´Ï´ÙMolecular dynamics¸¦ ÀÌ¿ëÇÏ¿© ƯÁ¤ ÈÇÐ ±¸Á¶¿¡ ´ëÇÑ conformerÀÇ ¾Ó»óºíÀ» »ý¼ºÇϰųª ½Ã°£¿¡ µû¸¥ ½Ã½ºÅÛÀÇ º¯È¸¦ È®ÀÎÇÒ ¼ö ÀÖ½À´Ï´Ù. º¹ÀâÇÑ ºÐÀÚÀÇ ½Ã¹Ä·¹À̼ǿ¡´Â ¸óÅ× Ä«¸¦·Î ¹æ¹ýº¸´Ù Molecular dynamicsÀÌ º¸´Ù È¿°úÀûÀÔ´Ï´Ù. Dynamics¢â´Â ÀÌ·¯ÇÑ ¾Ó»óºíÀ» »ý¼ºÇÏ´Â ¼ö ¸¹Àº Å×Å©´ÐÀ» Á¦°øÇÒ »Ó¸¸ ¾Æ´Ï¶ó °á°ú¹°ÀÇ ±¸¼ºÀ̳ª ºÐ¼®¿¡ ¿ä±¸µÇ´Â ±×·¡ÇÈ µµ±¸¸¦ Á¦°øÇÕ´Ï´Ù.
Key Benefits
· »ç¿ëÀÚ Á¤ÀÇ ÄÁÆ®·Ñ
· »ó¼¼ÇÑ ÀÌ·Ð ¼³¸í ¼³Á¤ ¹× °á°ú ºÐ¼®À» À§ÇÑ Á÷°üÀûÀÎ ±×·¡ÇÈ ÀÎÅÍÆäÀ̽º
· ¿ë¸Å 󸮸¦ À§ÇÑ ´Ù¾çÇÑ ¿É¼Ç Á¦°ø
· Simulated annealingÀÇ ´Ù¾çÇÑ ¹æ¹ý Á¦°ø
· Molecular Spreadsheet¢â ¿ÍÀÇ ±ä¹ÐÇÑ ÅëÇÕÀ¸·Î ½±°í ÀÚ¼¼ÇÑ ºÐ¼® °¡´É
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Radius of Gyration creates a "chrysanthemum" - a fatty acid is tethered at the carboxylate end of the molecule and followed for 50 picoseconds in a Dynamics run. The molecule is color-coded by the amount of local translation of the atoms.
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Advanced Computation
: Explore the Conformational Properties of Compounds
Overview
TriposÀÇ °·ÂÇÑ ºÐÀÚ µðÀÚÀÎ ±â´ÉÀº Advanced Computation™ µµ±¸¿Í ÇÔ²² ½ÃÀÛµÆ´Ù°í º¼ ¼ö ÀÖ½À´Ï´Ù. Áö¼ÓÀûÀÎ °í°´µéÀÇ ¼º°ø »ç·Ê´Â ÀÌ µµ±¸ÀÇ ¿ì¼ö¼ºÀ» Áõ¸íÇÕ´Ï´Ù. ÀÌ SYBYL¢ç ¸ðµâÀº ¾÷°è¿¡¼ °¡Àå ºü¸£°í À¯¿¬ÇÑ conformation °Ë»ö ¾Ë°í¸®ÁòÀ» Á¦°øÇϸç ÀÓÀÇ °Ë»ö ¹× grid °Ë»ö ±â´É ¶ÇÇÑ Á¦°øÇϰí ÀÖÀ¸¹Ç·Î »ç¿ëÀÚ´Â ÀÚ½ÅÀÇ conformation ºÐ¼® ¸ñÇ¥¿¡ ¸Â´Â ¹æ¹ýÀ» ¼±ÅÃÇÏ¸é µË´Ï´Ù.
Key Benefits
· ºÐÀÚ ³»¿¡¼ÀÇ Á¢±Ù ¹× ¿¡³ÊÁö¿¡ ±â¹ÝÇÑ ºÐ¼®
· ¾Ë·ÁÁø ¿øÀÚ °Å¸® ¹üÀ§ (e.g. NMR NOE data) ¶Ç´Â ´Ù¸¥ ºÐÀÚ¿¡¼ ¼öÇàµÇ¾ú´ø °á°ú¸¦ ÀÌ¿ëÇÑ constrain °Ë»ö
· Ring conformation °Ë»ö
· 2D NMRÀ̳ª Çü±¤ ÃøÁ¤ µî°ú °°Àº ½ÇÇè µ¥ÀÌÅÍ¿¡¼ ¾òÀº ¿øÀÚ °£ °Å¸®¸¦ ÀÌ¿ëÇÏ¿© 3D ±¸Á¶ Á¤ÀÇ. ´Ü¹éÁú ¹×
· ÆéŸÀÌµå µðÀÚÀο¡¼ ·çÇÁ conformation Ž»ö


GASP™(Genetic Algorithm Similarity Program)
: Complete Computational Chemistry and Molecular Modeling Environment
Genetic algorithmÀ» »ç¿ë conformationÀÌ À¯µ¿ÀûÀÎ ºÐÀÚµéÀ» superimposeÇÏ¿© alignment¸¦ »ý¼ºÇÏ´Â ÇÁ·Î±×·¥ÀÔ´Ï´Ù. ºÐÀÚ¿¡ ´ëÇÑ º°µµÀÇ »çÀü Á¤º¸, Áï pharmacophoric patternÀ̳ª constraint¸¦ ÇÊ¿ä·Î ÇÏÁö ¾Ê½À´Ï´Ù.
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Two angiotensin II receptor antagonists (2D degree view, left) and one of their alignments determined by GASP (stereo view, right). Pharmacophore elements are represented as purple spheres and include rings and receptor hydrogen bond sites. |
- Key Benefits
¤ýLigand ¼¼Æ®·ÎºÎÅÍ pharmacophore hypothesis¸¦ »ý¼ºÇÕ´Ï´Ù.
¤ý3D QSAR ¿¬±¸¸¦ À§ÇÑ ºÐÀÚ alignment¸¦ Á¦°øÇÕ´Ï´Ù.
¤ý°íÁ¤µÈ conformer ¼¼Æ®¸¦ ¹þ¾î³ª ´Ù¾çÇÑ conformationÀ» °í·ÁÇÒ ¼ö ÀÖ½À´Ï´Ù.
¤ýÀÛ¿ë±â(functional group)¿¡ ´ëÇÑ »çÀü Áö½ÄÀ» ÇÊ¿ä·Î ÇÏÁö ¾Ê½À´Ï´Ù.

GALAHAD¢ç
: Rapid, High Quality Pharmacophoric Perception and Molecular Alignments
»ý¹°ÇÐÀû Ȱ¼ºÀÇ common mode¸¦ °øÀ¯ÇÏ´Â ºÐÀÚµéÀ» alignÇÏ¿© ±×¿¡ ´ëÀÀÇÏ´Â pharmacophore hypothesis¸¦ »ý¼ºÇÕ´Ï´Ù. Á¤±³ÇØÁø »õ·Î¿î genetic algorithm°ú multi-objective scoring functionÀ» ÀÌ¿ëÇÏ¿© conformation À¯µ¿¼º, stereochemistry ºÒ¸íÈ®¼º, ring configuration ¼±Åüº, multiple partial match constraint, ºÐÀÚ°£ÀÇ feature mapping ¼±ÅüºÀ» Á¶ÀýÇÏ¸é¼ energetics, steric similarity, pharmacophoric overlapÀ» ¸ðµÎ °è»ê¿¡ ¹Ý¿µÇÏ¿© pharmacophore ¿ä¼Ò, constraint, ºÐÀÚ alignment¿¡ ´ëÇÑ »çÀü Áö½Ä ¾øÀÌ »õ·Î¿î target°ú »õ·Î¿î Ȱ¼º mode¸¦ Ž»öÇÒ ¼ö ÀÖ½À´Ï´Ù.
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GALAHAD model derived from four cyclin-dependent kinase (CDK2) inhibitors (left) vs. the overlay based on the corresponding X-ray crystal structures (right). |
Key Benefits
· Steric, pharmacophoric, energy Á¤º¸¸¦ µ¿½Ã¿¡ °í·ÁÇÏ´Â Pareto multi-objective optimizationÀ» »ç¿ëÇÏ¿© ¸ðµ¨ÀÌ ¾î´À ÇÑÂÊ¿¡ ÆíÁßµÇÁö ¾Ê½À´Ï´Ù.
· ´Ù¸¥ °è»ê ¹æ¹ýµé°ú ´Þ¸® °è»ê ½Ã°£ÀÌ ¸®°£µå ¼ö¿¡ ¼±ÇüÀ¸·Î ºñ·ÊÇÕ´Ï´Ù.
· Partial match/partial coverage ¸ðµ¨À» ¸¸µé ¼ö ÀÖ½À´Ï´Ù.

Tuplets™
: Pharmacophore-Based Virtual Screening without a 3D Model
TupletsÀº ºÐÀÚ ±¸Á¶ µ¥ÀÌÅÍ º£À̽º¿¡¼ Ȱ¼ºÀ» ¾Ë°í ÀÖ´Â ºÐÀÚ¿Í À¯»çÇÑ ÈÇÕ¹°À» ã¾Æ³»´Â ÇÁ·Î±×·¥ÀÔ´Ï´Ù. ¾Ë·ÁÁø Ȱ¼º ÈÇÕ¹°À̳ª Ȱ¼º/ºñȰ¼º ÈÇÕ¹°·Î ÀÌ·ç¾îÁø ¼¼Æ®, ȤÀº UNITY query¸¦ »ç¿ëÇÏ¿© °Ë»öÀ» ¼öÇàÇÒ ¼ö ÀÖ°í virtual combinatorial libraryÀÇ ¿ì¼±¼øÀ§¸¦ Á¤Çϴµ¥µµ »ç¿ëÇÒ ¼ö ÀÖ½À´Ï´Ù.
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A histogram (top left) is used to show the results of comparing a hypothesis (selected in the Tuplets - Hypothesis Manager - bottom center) to a reference set of 1,000 randomly selected drug-like molecules (shown in red) and the training set used to create the hypothesis (shown in green). The Tuplets - Analysis interface (top right) displays the number of compounds from both the training and reference sets that have a similarity greater than or equal to the specified cut-off value, allowing the user to interactively explore and apply multiple cut-off values for one or more similarity measurements. . |
Key Benefits
· ºÐÀÚ ±¸Á¶ µ¥ÀÌÅÍ º£À̽º¸¦ °Ë»öÇÏ´Â ¼Óµµ°¡ ±âÁ¸ÀÇ ÀüÅëÀûÀÎ 3D °Ë»ö ¼Óµµ¿¡ ºñÇØ ¼ö½Ê ¹è ÀÌ»ó ºü¸¨´Ï´Ù.
· HTS hitÀ̳ª Ȱ¼º/ºñȰ¼º ÈÇÕ¹°, UNITY query·ÎºÎÅÍ hypotheses¸¦ »ý¼ºÇÒ ¼ö ÀÖ½À´Ï´Ù.
· Pharmacophoric property¿¡ µû¶ó ÈÇÕ¹°À» ºÐ·ùÇÒ ¼ö ÀÖ½À´Ï´Ù.
· Reference µ¥ÀÌÅÍ ¼¼Æ®¿ÍÀÇ ºñ±³¸¦ ÅëÇØ query¸¦ characterization ÇÒ ¼ö ÀÖ½À´Ï´Ù.
· ÀüÅëÀûÀÎ 3D pharmacophore ¸ðµ¨À» ±¸ÇÏÁö ¾Ê¾Æµµ »ç¿ëÇÒ ¼ö ÀÖ½À´Ï´Ù.


Surflex-Sim
: Molecular Alignment and Virtual Screening
½Å¾à °³¹ßÀÇ ÁÖµÈ drug targetÀº ´Ü¹éÁúÀÇ 3D ±¸Á¶°¡ ¾Ë·ÁÁöÁö ¾ÊÀº °æ¿ì°¡ ´ëºÎºÐÀÔ´Ï´Ù. 3D ligand-based design Á¢±Ù¹ýÀº »ýüȰ¼º ¸®°£µåÀÇ conformation°ú alignmentÀÇ hypothesis¸¦ ¿¹Ãø ¹× Á¦°øÇÔÀ¸·Î ÀÌ·¯ÇÑ ¹®Á¦¸¦ ÇØ°áÇÏ°í »õ·Î¿î ¾à¹° Èĺ¸ ¹°ÁúÀ» µ¥ÀÌÅͺ£À̽º·ÎºÎÅÍ screening ÇÒ ¼ö ÀÖµµ·Ï ÇØÁÝ´Ï´Ù.
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Optimal superposition of nicotine and a competitive oxazole derivative by morphological similarity. The translucent grey surface represents the overall molecular volume of the aligned molecules. Differences in measurements made of nicotine and the oxazole derivative from the observation points in space are shown. The red surface indicates differences in the hydrophobic surfaces being observed, the blue and green surface indicates the high degree of electrostatic potential overlap between the two molecules. The major difference arises from the protruding methyl on the oxazole, not the difference in 2D substructure of the oxazole and pyridine. |
Surflex-SimÀº ºÐÀÚµéÀÇ 3D similarity¸¦ ÃÖ´ëÈÇÏ´Â alignment¸¦ ºü¸£°í ÀÚµ¿ÀûÀ¸·Î ÃÖÀûȽÃŵ´Ï´Ù. Surflex-Sim¿¡¼´Â alignµÈ ±¸Á¶ÀÇ Àüü ºÐÀÚ ºÎÇǸ¦ ÃÖ¼ÒÈÇÏ´Â surface-based morphological similarity functionÀ» »ç¿ëÇϴµ¥ ÀÌ·¯ÇÑ Á¢±Ù¹ýÀº »ý¹°ÇÐÀû Ȱ¼º¿¡ °ü·ÃµÈ ´Ù¸¥ ¹æ¹ýµé¿¡ ºñÇØ ´õ ¿ì¼öÇÑ °á°ú¸¦ º¸¿©ÁÝ´Ï´Ù.
Key Benefits
· Pairwise Alignment¿Í Morphological Similarity: Surflex-SimÀº ´ë»ó ºÐÀÚ¿¡ ´ëÇØ 3D similarity¸¦ ÃÖ´ëÈÇÏ´Â ¹æÇâÀ¸·Î queryÀÇ Æ÷Á ºü¸£°Ô ÃÖÀûȽÃŵ´Ï´Ù. À̶§ mappingÀ̳ª ¼³Á¤¿¡ ´ëÇÑ ÀÔ·Â °ªÀº ÇÊ¿äÇÏÁö ¾ÊÀ¸¸ç ºÐÀÚµéÀº »ùÇà ´Ü¹éÁú¿¡ bindingÇÏ´Â similarity °æÇâ¿¡ µû¶ó ÆÇ´ÜµË´Ï´Ù.
· Optimal Ensemble Alignment: Surflex-SimÀº ÀûÀº ¼öÀÇ °æÀïÀûÀÎ ¸®°£µå¿¡ ´ëÇØ pairwise similarity´Â ÃÖ´ëÈÇϰí Àüü ºÎÇÇ´Â ÃÖ¼ÒÈÇÏ ´Â optimal superpositionÀ» »ý¼ºÇÕ´Ï´Ù.
Validation
· Surflex-SimÀº ±¤¹üÀ§ÇÏ°í ´Ù¾çÇÑ target¿¡ °ÉÃÄ 59°³ÀÇ µ¥ÀÌÅÍ ¼¼Æ®¿¡ ´ëÇØ °ËÁõµÇ¾ú½À´Ï´Ù.
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The Receiver Operated Characteristic (ROC) Area Under the Curve (AUC) is a threshold independent score for the fractions of true positives and false negatives in compound screening. A ROC AUC of 1 represents a 100% true positive rate at a false positive rate of 0%. A ROC AUC of 0.5 represents a true positive and a false positive rate which is equivalent to a random selection. The displayed histogram summarizes the ROC scores for 59 arbitrary targets. The datasets used in the study are available for free download from
http://www.biopharmics.com/downloads.html |
Topomer Search
: Exceptionally Fast Ligand-Based Lead Hopping
Topomer Search´Â ÀÌ·ÊÀûÀ¸·Î ºü¸¥ 3D ligand-based virtual screening tool·Î lead hopping°ú scaffold hopping ¸ðµÎ¿¡ ´ëÇØ È¿°úÀûÀÓÀ» º¸¿©¿Ô½À´Ï´Ù. Topomer Search´Â ¼ö ¹é¸¸ °³ÀÇ ±¸Á¶µéÀ» ´ÜÀÏ ÇÁ·Î¼¼½º·Î ÇÏ·í¹ã ¸¸¿¡ °è»êÇÒ ¼ö ÀÖ°í, ¸Å¿ì Å« ÈÇÕ¹° µ¥ÀÌÅͺ£À̽º¸¦ screen ÇÒ ¼ö ÀÖÀ¸¸ç, subsetting ¶§¹®¿¡ Áß¿äÇÑ lead¸¦ ÀÒÀ» À§ÇèÀÌ ¾ø½À´Ï´Ù. conformation¿¡ ±¸¾Ö ¹ÞÁö ¾Ê´Â topomer similarity¸¦ ÀÌ¿ëÇØ whole molecule, side chain, scaffold¿¡ ´ëÇÑ screeningÀÌ ¸ðµÎ °¡´ÉÇϰí lead ÃÖÀûȸ¦ À§ÇÑ R-group °Ë»ö¿¡µµ »ç¿ëµÉ ¼ö ÀÖ½À´Ï´Ù. ´Ù¸¥ ¹æ¹ýµé°ú ´Þ¸® °è»ê ¼º´É¿¡ Å« ¿µÇâÀ» ÁÖ´Â ÀϾøÀÌ ¿©·¯ °³ÀÇ query¸¦ °Ë»ö¿¡ »ç¿ëÇÒ ¼ö ÀÖ°í leadÀÇ »ýüȰ¼ºÀ» °®´Â conformation¿¡ ´ëÇÑ »çÀüÁö½ÄÀ» ÇÊ¿ä·Î ÇÏÁö ¾Ê½À´Ï´Ù.
Key Benefits
01. EFFECTIVE
· Virtual Screening
· Lead Hopping°ú Scaffold Hopping
· R-Group°ú Scaffold °Ë»ö
02. FAST
· ¼ö ¹é¸¸ °³ÀÇ ±¸Á¶¸¦ ÇÏ·í¹ã ¸¸¿¡ °è»êÇÒ ¼ö ÀÖ½À´Ï´Ù.
· Subsetting ÇÏÁö ¾Ê°í Å« µ¥ÀÌÅÍ º£À̽º¸¦ screen ÇÒ ¼ö ÀÖÀ¸¹Ç·Î Áß¿äÇÑ lead¸¦ ÀÒÀ» À§ÇèÀÌ ¾ø½À´Ï´Ù.
· Multiple query °Ë»öÀÌ ºü¸£°í È¿À²ÀûÀ̹ǷΠ¾Ë·ÁÁø ¸ðµç lead¸¦ query·Î »ç¿ëÇÒ ¼ö ÀÖ½À´Ï´Ù.
03. EASY
· Query¿Í µ¥ÀÌÅͺ£À̽º conformationÀÌ µ¶¸³ÀûÀÔ´Ï´Ù.
· Feature mapping inputÀ̳ª guidance°¡ ÇÊ¿äÇÏÁö ¾Ê½À´Ï´Ù.
· Template Á¤Àǰ¡ ÇÊ¿äÇÏÁö ¾Ê½À´Ï´Ù.
· Protein preparationÀÌ ÇÊ¿äÇÏÁö ¾Ê½À´Ï´Ù.


QSAR with CoMFA¢ç
: Create & Visualize Structure-Activity Relationships That Accurately Predict Ligand Affinity
QSAR´Â moleculeÀÌ °®´Â ±¸Á¶ÀûÀΠƯ¼º(structure)°ú ±×¿¡ ÀÇÇØ ¹ßÇöµÇ´Â »ý¹°ÇÐÀû Ȱ¼º°ª(activity) »çÀÌÀÇ »ó°ü °ü°è¸¦ Á¤·®ÀûÀ¸·Î ±ÔÁ¤ÇÏ´Â °Í ÀÔ´Ï´Ù. SYBYL¿¡¼´Â ÈÇÕ¹°ÀÇ µ¥ÀÌÅ͸¦ ´Ù°¢ÀûÀ¸·Î ó¸®ÇÒ ¼ö ÀÖ´Â ±â´ÉÀ» Ȱ¿ëÇÏ¿© ´Ùº¯·® Åë°è ºÐ¼®(multivariate statistical analysis)À» ÅëÇØ ¸Å¿ì ³ôÀº ¼öÁØÀÇ QSAR ¸ðµ¨À» »ý¼ºÇÒ ¼ö ÀÖ½À´Ï´Ù. »ý¼ºµÈ ¸ðµ¨Àº ÀÏ·ÃÀÇ ÈÇÕ¹° ½Ã¸®Áî¿¡¼ ¾î¶² ±¸Á¶°¡ »ý¹°ÇÐÀû Ȱ¼º¿¡ ¾î¶»°Ô ±â¿©ÇÏ´ÂÁö¿¡ ´ëÇÑ ¼³¸íÀ» Á¦½Ã ÇÒ ¼ö ÀÖÀ¸¸ç, À̸¦ ±â¹ÝÀ¸·Î lead optimization¿¡ ´ëÇÑ ´Ù¾çÇÑ ¾ÆÀ̵ð¾î¸¦ ¾òÀ» ¼ö ÀÖ½À´Ï´Ù. »ý¹°ÇÐÀû Ȱ¼ºÀÌ ¾Ë·ÁÁöÁö ¾ÊÀº moleculeµéÀÇ activity ¿¹Ãøµµ À¯¿ëÇÑ ±â´É Áß ÇϳªÀÔ´Ï´Ù. SYBYL¿¡¼´Â CoMFA, CoMSIA, SIMCA µîÀÇ ¸ðµ¨À» »ý¼ºÇÏ´Â µµ±¸¿Í Principal Components Analysis, Hierarchical Clustering, Clustering Methods µîÀÇ ºÐ¼® µµ±¸¸¦ Á¦°øÇÕ´Ï´Ù. ¶ÇÇÑ, ºÐÀÚÀÇ ´Ù¾çÇÑ ¹°¸® ÈÇÐÀû Ư¼ºÀ» °è»êÇÏ´Â µµ±¸¸¦ »ç¿ëÇÏ¿© µ¥ÀÌÅÍ ¼¼Æ®¿¡ ´ëÇÑ ºÐ¼®À̳ª ¸ðµ¨¸µ¿¡¼ È¿°úÀûÀÎ descriptor·Î »ç¿ëÇÒ ¼ö ÀÖ½À´Ï´Ù ´Ù¾çÇÑ ±×·¡ÇÁ µµ±¸¸¦ ÀÌ¿ëÇÑ ½Ã°¢ÀûÀÎ ºÐ¼®µµ ¿ëÀÌÇÕ´Ï´Ù.
QSAR with CoMFA¢ç´Â 3D-QSARÀÇ ÀÏÁ¾À¸·Î moleculeÀÇ ±¸Á¶¸¦ ¼³¸íÇÏ´Â descriptor·Î CoMFA field¸¦ »ç¿ëÇÕ´Ï´Ù. CoMFA field¿¡´Â steric field¿Í electrostatic field°¡ ÀÖÀ¸¸ç ÀÌ·¯ÇÑ ºÐÀÚ ±¸Á¶¿¡ ÀÇÇÑ Æ¯¼º¿¡ ¿¬°üµÈ(»ý¹°ÇÐÀû Ȱ¼ºÀ» Æ÷ÇÔÇÏ¿©) Åë°èÀûÀÌ°í ½Ã°¢ÀûÀÎ ¸ðµ¨À» ¸¸µì´Ï´Ù. Tripos°¡ ƯÇã±ÇÀ» °¡Áø Comparative Molecular Field Analysis(CoMFA)´Â 30³â °¡±î¿î ½Ã°£ µ¿¾È ¼ö ¹é ÆíÀÇ QSAR °ü·Ã ¿¬±¸ ³í¹®¿¡¼ ¼±ÅÃµÇ¾î »ç¿ëµÇ¸é¼ ±× ´É·ÂÀÌ °ËÁõµÇ¾ú°í ÇöÀç±îÁöµµ ±× À¯¿ë¼ºÀ» ÀÎÁ¤¹Þ°í ÀÖ½À´Ï´Ù.
Contour plots from a CoMSIA analysis of thrombin inhibitors. (Left) Regions of favorable steric interactions are shown in green; sterically unfavorable regions are shown in yellow. (Right) Blue contours indicate regions where hydrophobic interactions enhance binding; red contours show regions where hydrophobic properties decrease affinity. These contours were used to design a novel inhibitor, displayed in blue, predicted to have ~100x greater affinity. The piperidine ring of the original inhibitor was enlarged to a decaline system in order to occupy regions that favor both steric bulk and hydrophobic groups. The methyl ester was changed to a methyl group to reduce unfavorable steric interactions while still occupying a region favorable for hydrophobic interactions.
Key Benefits
· Á¤·®ÀûÀÎ ±¸Á¶-Ȱ¼º °ü°è½ÄÀ» Àü°³ÇÕ´Ï´Ù.
· Å×½ºÆ® µÇÁö ¾ÊÀº ºÐÀÚÀÇ Æ¯¼º°ú Ȱ¼ºÀ» ¿¹ÃøÇÕ´Ï´Ù.
· Lead ÈÇÕ¹°ÀÇ Æ¯¼ºÀ» ÃÖÀûÈ ½Ãŵ´Ï´Ù.
· Receptor binding site ¸ðµ¨À» validation ÇÒ ¼ö ÀÖ½À´Ï´Ù.
· Receptor binding siteÀÇ Æ¯¼º¿¡ ´ëÇÑ hypothesis¸¦ »ý¼ºÇÕ´Ï´Ù.
· ÇÕ¼ºÇϰųª screen ÇÒ ÈÇÕ¹°ÀÇ ¿ì¼±¼øÀ§¸¦ Á¤ÇÒ ¼ö ÀÖ½À´Ï´Ù.
· ³ôÀº receptor-ligand ģȵµ¿¡ ¿ä±¸µÇ´Â ÇÙ½É ±¸Á¶¸¦ Á¤ÀÇÇÒ ¼ö ÀÖ½À´Ï´Ù.
Advanced CoMFA
: Expert Tools for QSAR Analysis and Lead Optimization
QSAR ¸ðµâÀÇ ÆÄ¿ö¸¦ Áõ°¡½ÃŲ °ÍÀ¸·Î Spreadsheet »óÀÇ QSAR ¸Þ´º ¾È¿¡¼ »ç¿ëÇÒ ¼ö ÀÖ½À´Ï´Ù. ¸ðµ¨À» ºÐ¼®ÇÏ´Â tool°ú field¸¦ »ý¼ºÇÏ´Â tool, ±×¸®°í ¸ðµ¨À» »ý¼ºÇÏ´Â tool °¢°¢À» º¸´Ù Á¤È®ÇÏ°í ºü¸£°Ô ¼öÇàÇÒ ¼ö ÀÖµµ·Ï ÇÕ´Ï´Ù. Field¸¦ »ý¼ºÇÏ´Â CoMFA¿¡¼´Â °¢ probe¿¡¼ °è»êµÈ steric°ú electrostatic termÀ» ´Ù°¢ÀûÀ¸·Î ó¸®ÇÏ¿© modeÀ» °è»êÇϴµ¥ ºÒÇÊ¿äÇÑ ºÎºÐÀ» ¿©°úÇÔÀ¸·Î½á noise¸¦ ÁÙÀÏ ¼ö ÀÖ¾î, °è»êÀ» º¸´Ù Á¤È®ÇÏ°í ºü¸£°Ô ¼öÇàÇÒ ¼ö ÀÖ½À´Ï´Ù. ±âº»ÀûÀÎ toolÀº Hierarchical Clustering, Region Focusing, QSAR Optimization, Analysis Notes, Progressive Scrambling, Jarvis-Patrick ClusteringµîÀÌ ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ expert analysis toolÀ» ÅëÇØ Ȱ¼ºÀ» ¼º°øÀûÀ¸·Î ¿¹ÃøÇÒ ¼ö ÀÖ°Ô µµ¿ÍÁÙ ´õ ¸¹Àº Á¤º¸¸¦ ¾òÀ» ¼ö ÀÖ½À´Ï´Ù. ¶ÇÇÑ, Advanced CoMFA¿¡¼ Á¦°øµÇ´Â expert graphics toolÀº ´Ù¸¥ ÆÀ ¸â¹öµé°úÀÇ ³íÀdzª ´ë±Ô¸ðÀÇ Çмú ȸÀÇ¿¡¼ º¸´Ù Æí¸®ÇÑ ÀÇ»ç¼ÒÅëÀÌ °¡´ÉÇϵµ·Ï ±â¿©ÇÒ ¼ö ÀÖ½À´Ï´Ù.
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Advanced CoMFA allows focusing of standard steric and electrostatic CoMFA fields on spatial regions which best describe variation in biological activity. |
Key Benefits
· ´õ ¸¹Àº field class¿Í cluster management toolÀ» Á¦°øÇÕ´Ï´Ù.
· ¾àÇÑ ¸ðµ¨À» °³¼±½ÃŰ´Â Å×Å©´ÐÀÔ´Ï´Ù.
· ¸ðµ¨ÀÇ ´É·Â°ú ½Ã°¢ÀûÀÎ ¸í·á¼º Áõ´ë¸¦ À§ÇÑ Region FocusingÀ» Áö¿øÇÕ´Ï´Ù.
· ºÐ¼® °á°ú¸¦ º¸°í µû¶ó°¡´Â °úÁ¤ÀÌ Æí¸®ÇØÁ³½À´Ï´Ù.
· °¡´É¼º ÀÖ´Â lead¿¡ ´ëÇÑ combinatorial library °Ë»öÀÌ °¡´ÉÇÕ´Ï´Ù.
Topomer CoMFA¢ç
: Effortless 3D QSAR
Topomer CoMFA¢ç´Â »ý¹°ÇÐÀû Ȱ¼ºÀ̳ª ÈÇÕ¹° Ư¼ºÀ» ¿¹ÃøÇÏ´Â ¸ðµ¨À» ¼ö ºÐ(minute) ¾È¿¡ ÀÚµ¿ÀûÀ¸·Î »ý¼ºÇÏ´Â 3D QSAR toolÀÔ´Ï´Ù. Alignment¿¡ ´ëÇÑ ÀÚüÀûÀÎ ±ÔÄ¢À» ³»ÀåÇϰí ÀÖÀ¸¹Ç·Î ºÐÀÚ alignment³ª conformer ¼±Åÿ¡ ´ëÇÑ Àü¹®ÀûÀÎ Áö½ÄÀ̳ª º°µµÀÇ Áغñ¸¦ ÇÊ¿ä·Î ÇÏÁö ¾Ê½À´Ï´Ù. µû¶ó¼ ´Ù¸¥ 3D QSAR ¹æ¹ýµé¿¡ ºñÇØ ½Ã°£ÀÌ ´ú °É¸®°í QSAR ¼÷·ÃÀÚ³ª ºñ¼÷·ÃÀÚ ¸ðµÎ ½±°Ô »ç¿ëÇÒ ¼ö ÀÖÀ¸¸ç °á°úµµ ÀüÅëÀûÀÎ CoMFA ºÐ¼® °á°ú¿¡ ºñÇØ µÚ¶³¾îÁöÁö ¾Ê½À´Ï´Ù. Topomer CoMFA¿¡¼ ¾òÀº Á¤·®ÀûÀÎ 3D QSAR ¿¹Ãø ¸ðµ¨Àº Topomer Search¿¡¼ ÈÇÕ¹° Ȱ¼ºÀÇ ÃÖÀûÈ¿¡ ÇÊ¿äÇÏ´Ù°í ¿¹ÃøµÈ ±âº»°ñ°Ý ±¸Á¶(substructure)¿Í R-groupÀÇ ºü¸¥ °Ë»ö°ú È®ÀÎÀ» À§ÇØ »ç¿ëµÉ ¼ö ÀÖ½À´Ï´Ù.
Key Benefits
01. EFFECTIVE
· CADD ¿¬±¸ÀÚµéÀº Topomer CoMFA ¸ðµ¨ÀÇ ¿¹ÃøÀÌ °¡´É¼ºÀÌ ÀÖ´Â °ÍÀÎÁö ½Å¼ÓÈ÷ Á¤ÀÇÇÒ ¼ö ÀÖ½À´Ï´Ù.
· Topomer CoMFA¿¡¼ ¾ò¾îÁø Á¤·®ÀûÀÎ 3D QSAR ¿¹Ãø ¸ðµ¨Àº Topomer Search¿¡¼ »ç¿ëµÉ ¼ö ÀÖ°í, Topomer Search¿¡¼´Â ÀüüºÐÀÚ, R-group, scaffold¿¡ ´ëÇÑ °Ë»öÀÌ ¸ðµÎ °¡´ÉÇÕ´Ï´Ù.
· ´ëü·Î °á°ú°¡ ÀüÅëÀûÀÎ CoMFA ºÐ¼®¿¡ ºñÇØ µÚ¶³¾îÁöÁö ¾Ê½À´Ï´Ù.
02. FAST
· °á°ú¸¦ °ÅÀÇ ¼ö ºÐ(minute) ¾È¿¡ ¾òÀ» ¼ö ÀÖ½À´Ï´Ù.
· ±¸Á¶ alignment°¡ ÀÚµ¿ÀûÀ¸·Î ¼öÇàµÇ¹Ç·Î ±âÁ¸ÀÇ 3D-QSAR ºÐ¼®¿¡ ºñÇØ ½Ã°£ÀÌ Àý¾àµË´Ï´Ù.
· Ãʺ¸ÀÚ¿Í Àü¹®°¡ ¸ðµÎ¿¡°Ô »ç¿ëÀÌ ½±°í Æí¸®ÇÕ´Ï´Ù.
HQSAR™
: Perform Automated QSAR Analyses
Hologram QSAR(HQSAR™)´Â molecular hologram°ú PLS¸¦ »ç¿ëÇØ fragment¿¡ ±â¹ÝÇÑ ±¸Á¶-Ȱ¼º °ü°è½ÄÀ» »ý¼ºÇÕ´Ï´Ù. ºÐÀÚÀÇ alignment¸¦ ÇÊ¿ä·Î ÇÏÁö ¾ÊÀ¸¸ç, ¸Å¿ì Å« µ¥ÀÌÅÍ ¼¼Æ®ÀÇ ÀÚµ¿ÈµÈ ºÐ¼®À» °¡´ÉÇÏ°Ô ÇÕ´Ï´Ù.
HQSAR¿¡¼´Â ºÐÀÚ ±¸Á¶°¡ 2D fingerprint·Î ÄÚµù µÇ°í, ºÐÀÚ ±¸Á¶´Â ºÐÀÚÀÇ Æ¯¼º(»ý¹°ÇÐÀû Ȱ¼ºÀ» Æ÷ÇÔÇÏ¿©)À» °áÁ¤ÇÏ´Â ±âº» ¿äÀÎÀ̹ǷΠfingerprint·ÎºÎÅÍ ºÐÀÚÀÇ È°¼ºÀ» ¿¹ÃøÇÒ ¼ö ÀÖ½À´Ï´Ù. ¿©·¯ validation ¿¬±¸¸¦ ÅëÇØ HQSAR°¡ º¸´Ù Á¤±³ÇÑ 3D-QSAR ¹æ¹ýµé¿¡ ºñÇØ »ç¿ëÀº ÈξÀ ½¬¿ì¸é¼µµ µÚ¶³¾îÁöÁö ¾Ê´Â ¿¹Ãø ´É·ÂÀ» °¡Áø´Ù´Â °ÍÀÌ Áõ¸íµÇ¾ú½À´Ï´Ù.
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HQSAR models can be readily interpreted in chemical terms from color-coded atoms in molecular fragments that make a positive or negative contribution to activity |
Key Benefits
· »ý¹°ÇÐÀû Ȱ¼º ¶Ç´Â Ư¼º°ú ÈÇÐÀû ±¸Á¶ÀÇ °ü°è¸¦ ÀÚµ¿À¸·Î Á¤·®ÀûÀÎ ¸ðµ¨·Î ¸¸µì´Ï´Ù.
· 3D ±¸Á¶, conformation, alignment µîÀ» ÀÔ·ÂÇÏÁö ¾Ê¾Æµµ µË´Ï´Ù.
· ÀϹÝÀûÀÎ Å©±âÀÇ µ¥ÀÌÅͺ£À̽º»Ó¸¸ ¾Æ´Ï¶ó 10¸¸°³ Á¤µµÀÇ °Å´ëÇÑ µ¥ÀÌÅͺ£À̽º¿¡¼µµ »ç¿ëÀÌ °¡´ÉÇÕ´Ï´Ù.
· µ¥ÀÌÅÍ ¼¼Æ®ÀÇ SAR profileÀ» ½Å¼ÓÇÏ°Ô È®ÀÎÇÒ ¼ö ÀÖ½À´Ï´Ù.
· Ȱ¼º¿¡ ¿µÇâÀ» ¹ÌÄ¡´Â Áß¿äÇÑ Æ¯¼ºÀ» °¡Áø fragment¸¦ positive ¶Ç´Â negative contribution ÇÏ´Â Á¤µµ¿¡ µû¶ó color-codingµÈ ÇüÅ·Πº¸¿©ÁÝ´Ï´Ù. ¾ò¾îÁø fragment´Â »õ·Î¿î ÈÇÕ¹° ÇÕ¼ºÀÇ ´Ü¼°¡ µÇ°Å³ª ÀÌ¹Ì °ËÁõµÈ ÈÇÕ¹°À» ¸¸µé±â À§ÇØ ¾î¶»°Ô modify ½ÃÄÑ¾ß ÇÏ´ÂÁö¿¡ ´ëÇÑ ÈùÆ®°¡ µÉ ¼ö ÀÖ½À´Ï´Ù.
· ÈÇÐ ±¸Á¶ ÁýÇÕ¿¡ ´ëÇÑ ¿¹ÃøÀ» À§ÇØ µ¥ÀÌÅͺ£À̽º °Ë»öÀ» Áö¿øÇÕ´Ï´Ù.
VolSurf™
: Calculate ADME Properties and Create Predictive ADME Models
½Å¾à°³¹ßÀÇ °ÅÀÇ ÃÖÁ¾´Ü°è¿¡¼ ½Å¾à Èĺ¸ÀÇ »ó´ç¼ö°¡ ½ÇÆÐÇÑ´Ù´Â »ç½ÇÀº ¸¹Àº ½Ã°£°ú ÀÚ±ÝÀÌ Å×½ºÆ®¿¡ ÅõÀڵDZâ Àü¿¡ ºÎÀûÀýÇÑ ÈÇÕ¹°À» ¹èÁ¦ÇÒ ¼ö ÀÖ´Â ¿¹Ãø µµ±¸µéÀÇ Çʿ伺À» ºÎ°¢½ÃÄ×½À´Ï´Ù. VolSurf™´Â °ËÁõµÈ in vitro ½ÇÇè µ¥ÀÌÅ͵éÀ» Åä´ë·Î ÇÑ ¾à¹°ÀÇ in vivo Ȱ¼º °è»êÀ¸·Î ¹Ì¸® ±¸ÇسõÀº ¸ðµ¨µéÀ» »ç¿ëÇÏ¿© ADME(Absorption, Distribution, Metabolism, Excretion) Ư¼ºÀ» ¿¹ÃøÇÏ´Â °è»ê µµ±¸·Î, ƯÁ¤ ADME °ü·Ã descriptorµéÀ» °è»êÇÏ°í »ýüȰ¼º ¹× Ư¼º¿¡ ´ëÇÑ ¿¹Ãø ¸ðµ¨ »ý¼ºÀ» À§ÇÑ Åë°èÀû ºÐ¼®À» ¼öÇàÇÕ´Ï´Ù. VolSurf¿¡ Æ÷ÇÔµÈ ADME ¸ðµ¨µéÀº ¾à¹° ¿ëÇØµµ¿Í Caco-2 ¼¼Æ÷ Èí¼ö, Ç÷°ü-³úÀ庮 Åõ°úµµ(blood-brain barrier permeation), ¾à¹° ºÐÆ÷¸¦ ¿¹ÃøÇÕ´Ï´Ù.
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The predicted versus actual percent Human Intestinal Absorption (%HIA) for a set of passively absorbed compounds determined by VolSurf. The r2 and q2 values for this model are 0.82 and 0.73, respectively. VolSurf's chemically intuitive descriptors are based on 3D molecular fields. Examples include the hydrophobic (bottom right, in blue) and hydrophilic surface area of diazepam (upper left, in red). In the latter, the solvent accessible surface is shown as a transparent yellow surface. |
Key Benefits
·Virtual screening toolÀÇ »ç¿ëÀ» À§ÇÑ ºü¸¥ ¿¹ÃøÀÌ °¡´ÉÇÕ´Ï´Ù.
·Volsurf descriptorµéÀ» ÀÌ¿ëÇÑ ¸ðµ¨µéÀº ´Ù¸¥ descriptorµéÀ» ÀÌ¿ëÇØ »ý¼ºµÈ ¸ðµ¨µé º¸´Ù È®¿¬È÷ ¶Ù¾î³ ¿¹Ãø·ÂÀ» º¸ÀÔ´Ï´Ù.
·QSAR with CoMFA¢çÀÇ 2D/3D descriptorµéÀ» º¸¿ÏÇÕ´Ï´Ù.
·¸ðµ¨ »ý¼º¿¡ ºÐÀÚ alignment°¡ ÇÊ¿äÇÏÁö ¾Ê½À´Ï´Ù.
·GRID³ª CoMFA field¸¦ ÈÇÐÀûÀ¸·Î Áö°¢ÇÒ ¼ö ÀÖ´Â descriptorµé·Î º¯È¯ÇÕ´Ï´Ù.
·»ý¼ºµÈ ¸ðµ¨µéÀº conformational sampling¿¡ ¿µÇâÀ» ¹ÞÁö ¾Ê½À´Ï´Ù.


Biopolymer
: Predict, Build, and Visualize Macromolecular 3D Structure
Biopolymer™´Â ÆéƼµå, ´Ü¹éÁú, DNA/RNA, carbohydrate ±¸Á¶µéÀ» ¸¸µé°í ´Ù·ê ¼ö ÀÖ´Â µµ±¸¸¦ Á¦°øÇÕ´Ï´Ù. Biopolymer´Â SYBYL¢çÀÇ ±¸Á¶ ¼³°è, refinement, ½Ã°¢È, ºÐ¼® µîÀÇ ±â´É°ú ¿ÏÀüÈ÷ ÅëÇÕÇÏ¿© »ç¿ëµÉ ¼ö ÀÖ½À´Ï´Ù. ´Ü¹éÁú ±¸Á¶´Â SYBYL Molecular SpreadsheetÀ» ÀÌ¿ëÇÏ¿© ¼¼ºÎÀûÀ¸·Î ºÐ¼®ÇÒ ¼ö ÀÖ½À´Ï´Ù.
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The methyl glycoside of the Lewis b human blood group determinant complexed with Lectin IV of Griffonia simplicifolia. The protein is represented as tubes color-coded by secondary structure type. The tetrasaccharides are shown as space-filled atoms. |
Key Benefits
· ÆéƼµå, ´Ü¹éÁú, RNA, DNA, ´Ù´ç·ùÀÇ ¸ðµ¨À» ¸¸µé ¼ö ÀÖ½À´Ï´Ù.
· °¡»ó µ¹¿¬º¯ÀÌ À¯¹ß(Virtual mutagenesis) ½ÇÇèÀÌ °¡´ÉÇÕ´Ï´Ù.
· Protein Data BankÀÇ ÆÐÅÏ °Ë»öÀ» Áö¿øÇÕ´Ï´Ù.
· °áÁ¤ÇÐ, NMR, ¶Ç´Â ¸ðµ¨¸µÀ¸·ÎºÎÅÍ ¾òÀº ±¸Á¶µéÀ» refine ½Ãų ¼ö ÀÖ½À´Ï´Ù.
· ±¸Á¶-Ȱ¼º °ü°è¸¦ º¸¿©ÁÖ´Â ±×·¡ÇÈ °á°ú¸¦ »ý¼ºÇÕ´Ï´Ù.
· ¸®°£µå°¡ ¼ö¿ëü¿¡ bindingÇÏ´Â conformationÀ» ã¾Æ³À´Ï´Ù.
Advanced Protein Modeling (APM)
: Find Homologs by Sequence-Structure Comparison and Construct 3D models from Protein Sequences
´Ü¹éÁú ¿°±â¼¿°ú ÀÌ¹Ì ¾Ë·ÁÁø ´Ü¹éÁú ±¸Á¶ »çÀÌÀÇ »óµ¿°ü°è(homology)¸¦ ÀÎÁöÇÏ´Â °ÍÀº Ư¼ºÀÌ ¹àÇôÁöÁö ¾ÊÀº ¿°±â¼¿ÀÇ »ý¹°ÇÐÀû ¼ºÁú ¹× »ýÈÇÐÀû ±â´ÉÀ» ÀÌÇØÇϴµ¥ ¸Å¿ì À¯¿ëÇÑ Á¤º¸¸¦ Á¦°øÇϸç, ºñ±³ ¸ðµ¨¸µ(comparative modeling, homology modeling)À» ÅëÇÑ ´Ü¹éÁúÀÇ 3Â÷¿ø ±¸Á¶ ¿¹ÃøÀ» °¡´ÉÇÏ°Ô ÇÕ´Ï´Ù. SYBYLÀÇ Advanced Protein Modeling ÆÐŰÁö´Â È¿À²ÀûÀ¸·Î ¿¬µ¿µÇ´Â ÀÎÅÍÆäÀ̽º¸¦ ÅëÇØ »ç¿ëÀÚ°¡ homolog Ž»ö°ú ºñ±³ ¸ðµ¨¸µ ¸ðµÎ¸¦ ¼öÇàÇÒ ¼ö ÀÖµµ·Ï ÇÏ´Â µµ±¸ÀÔ´Ï´Ù.
Using the FUGUE™ technology, structural homologs of a target sequence can be identified by sequence-structure comparison using a database of detailed structural profiles of all known protein families. A protein family's structural profile comprises an environment-specific substitution table that takes into account secondary structure, solvent accessibility and hydrogen-bonding interactions, and structure-dependent gap penalties. These tables are derived from the structural alignments of experimentally determined protein structures in HOMSTRAD¢â, a database containing thousands of 3D protein structures clustered into related families. The key elements of FUGUE are environment-specific substitution tables, structure-dependent gap penalties, automated alignment method selection, and HOMSTRAD.
Using the ORCHESTRAR™ technology, comparative models can be built from a target sequence using single or multiple structural homologs found by FUGUE or provided by the user. In this process, sequence alignment is paramount to the success of model building. The ORCHESTRAR interface in SYBYL allows the user to align sequences using homology, local structural environment, manual editing, or import from external tools. Homologs are structurally aligned based using homology and local structural environment, and then structurally conserved regions are identified using backbone curvature and torsion in addition to C-alpha rmsd and homology. Loops (structurally variable regions) are then modeled by knowledge-based or ab initio approaches, and sidechains are added by enumerating rotamer combinations constrained by borrowing as much information from the parent homologs as possible. Finally, a range of analysis tools are available to highlight potential problems with the structure and allow the user to iterate through the process and refine initial models.
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Comparison of homology model of human factor Xa (Stuart-Prower factor, white) to the crystal structure (2BOK.pdb, orange), created using Advanced Protein Modeling. Sequence identities between the target and the five homologs used in modeling ranged between 40%-80%. The RMSD between the backbones of the model and the crystal structure is 1.5 A. |
Key Benefits
· Identify and validate an uncharacterized target sequence based on its association with known protein families.
· Generate knowledge useful in selecting compounds for screening or virtual screening experiments.
· Derive accurate sequence alignments and produce 3D models of the target and/or as the basis for structure-based design and virtual screening studies.
· Interactively visualize multiple sequence alignments and corresponding 3D protein structures.
· Streamlined workflow to guide the user through the complex comparative modeling process.
· Align structures based on local structural environment as well as homology.
· Find structurally conserved regions based on multiple homolog templates using curvature and torsion.
· Use knowledge-based methods in conjunction with ab initio methods to find the best loops and sidechain conformations.
· Focus on each key step to improve model success or click-through each step with default settings to quickly produce a rough model.


Legion™
: Build and Store Virtual Combinatorial Libraries
Legion™Àº ´Ù¾ç¼ºÀ» °¡Áø screening ¶Ç´Â lead ÃÖÀûÈ¿¡ ÃÊÁ¡À» µÐ library¸¦ µðÀÚÀÎÇϴµ¥ »ç¿ëµÉ ¼ö ÀÖ´Â virtual combinatorial library¸¦ »ý¼ºÇϸç, cyclization, cycloaddition, rearrangement µîÀ» Æ÷ÇÔÇÏ¿© ¾î¶² Á¾·ùÀÇ º¹ÇÕ ¹ÝÀÀµµ ¼³¸íÇÒ ¼ö ÀÖ½À´Ï´Ù. »ç¿ëÀÚ°¡ ÁöÁ¤ÇÑ chirality, regiospecificity, stereospecificity¸¦ À¯ÁöÇÏ¸é¼ library¸¦ ¸¸µé ¼ö ÀÖ°í, ¹ÝÀÀÀÇ regio- ¶Ç´Â stereoselectivity°¡ È®½ÇÇÏÁö ¾ÊÀº °æ¿ì¿¡´Â library¿¡¼ ¸ðµç ¶Ç´Â ¸î¸îÀÇ »ý¼º¹°À» ġȯÇÒ ¼ö ÀÖ½À´Ï´Ù. À̶§ º¯µ¿ site´Â ´Ü¼øÇÑ R-group ġȯ¿¡¼ º¹ÀâÇÑ multivalent ġȯ±îÁö °¡´ÉÇÕ´Ï´Ù.
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The core structure and some of its variants in a virtual library of metalloproteinase inhibitors built using Legion. |
Key Benefits
· Virtual screeningÀ» À§ÇÑ combinatorial library¸¦ ¸¸µì´Ï´Ù.
· ScreeningÀ̳ª lead ÃÖÀûÈ¿¡ ÃÊÁ¡À» µÐ library¸¦ ¸¸µé±â À§ÇÑ ´Ù¾çÇÑ libraryÀÇ ¼Õ½¬¿î µðÀÚÀÎÀÌ °¡´ÉÇÕ´Ï´Ù.
· ÀÌÈÄÀÇ °è»êÀ» À§ÇÑ Èĺ¸ library¸¦ »ý¼ºÇϰí ÀúÀåÇÕ´Ï´Ù.
CombiLibMaker™
: Generate Virtual Combinatorial Libraries
Á¶ÇÕÈÇÐ(Combinatorial chemistry)Àº ´ë·®ÀÇ ÈÇÕ¹° library ÇÕ¼ºÀ» ÀÌ·ÐÀûÀ¸·Î °¡´ÉÇÏ°Ô ÇÕ´Ï´Ù. ÀϹÝÀûÀ¸·Î ½Ã°£Àû, ºñ¿ëÀû ÀÚ¿øÀÇ Á¦ÇÑ¿¡ ÀÇÇØ ÁÖ¾îÁø ¹ÝÀÀÀ¸·Î »ý¼ºµÉ ¼ö ÀÖ´Â ÈÇÕ¹° Áß ÀϺθ¸À» ÇÕ¼ºÇϰí screen ÇÒ ¼ö ÀÖÀ¸¹Ç·Î ¾î¶² ÈÇÕ¹°À» ¸¸µé °ÍÀÎÁö, ¶Ç´Â ¾î¶² ÈÇÕ¹°À» Å×½ºÆ® ÇÒ °ÍÀÎÁö¸¦ °áÁ¤Çϴµ¥ °¡»óÀÇ combinatorial library°¡ Á¡Á¡ ´õ ¸¹ÀÌ ÀÌ¿ëµÇ°í ÀÖ½À´Ï´Ù. CombiLibMaker¢â´Â ÀÌ·¯ÇÑ libraryµéÀ» ¸¸µé°í DiverseSolutions¢çÀ̳ª Selector¢â°ú ÇÔ²² diverse ¶Ç´Â focused library¸¦ µðÀÚÀÎ Çϱâ À§ÇØ »ç¿ëµË´Ï´Ù. ¶ÇÇÑ Concord¢ç¸¦ ÀÌ¿ëÇÏ¿© CoMFA¢ç¿Í °°Àº 3D QSARÀÇ È°¼º ¿¹ÃøÀ» ÆíÇÏ°Ô Çϰųª °¡»ó Ãʰí¼Ó °Ë»ö(vHTS)¿¡¼ combinatorial productÀÇ docking°ú scoring ¼Óµµ¸¦ Çâ»ó½Ãų ¼ö ÀÖ´Â core-align µÈ 3D ±¸Á¶¸¦ »ý¼ºÇÒ ¼ö ÀÖ½À´Ï´Ù.
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Diazoles generated by CombiLibMaker in UNITY¢ç 3D hitlist format with product cores aligned. |
Key Benefits
· ±â¾÷ µ¥ÀÌÅͺ£À̽º¿¡ µî·ÏÇϱâ À§ÇÑ combinatorial productµéÀ» »ý¼ºÇϰí À̸§À» ºÙÀÔ´Ï´Ù.
· °¡»ó Ãʰí¼Ó °Ë»ö(vHTS)À» À§ÇÑ combinatorial library¸¦ ¸¸µì´Ï´Ù.
· Lead ÈÇÕ¹°¿¡ °¡±î¿î °¡»óÀÇ ÈÇÕ¹°µéÀ» »ý¼ºÇÕ´Ï´Ù.
· ÇÕ¼ºÇÒ subsetÀ» ¼±ÅÃÇÒ ¼ö ÀÖµµ·Ï Èĺ¸ ÈÇÕ¹°µéÀ» ¿°ÅÇÕ´Ï´Ù.
· °¡´É¼º ÀÖ´Â lead¿¡ ´ëÇÑ combinatorial library °Ë»öÀÌ °¡´ÉÇÕ´Ï´Ù.
· Diverse ¶Ç´Â focused library µðÀÚÀÎÀÌ °¡´ÉÇÕ´Ï´Ù.
DiverseSolutions¢ç
: Design, Compare, or Select Compound Libraries
¿¬±¸ÀÇ È¿À²¼º°ú »ý»ê¼º Çâ»óÀ» À§ÇÑ ½Å¾à°³¹ß °úÁ¤ÀÇ ÇÙ½É ±â¼ú·Î¼ ºÐÀÚÀÇ ´Ù¾ç¼ºÀ» °í·ÁÇÑ ÈÇÕ¹° libraryÀÇ Ãʰí¼Ó(HTP) ÇÕ¼º°ú °Ë»ö¿¡ °ü½ÉÀÌ ÁýÁߵǰí ÀÖ½À´Ï´Ù. DiverseSolutions¢çÀº ÈÇÕ¹°ÀÇ ´Ù¾ç¼ºÀ» ´Ù·ç±â À§ÇÑ ÈÇÐÀûÀ¸·Î Á÷°üÀûÀÎ toolµéÀÇ ÁýÇÕÀ¸·Î chemical diversity¸¦ ±âº» °³³äÀ¸·Î ÇÏ¿© µ¥ÀÌÅͺ£À̽º¿¡ ÀúÀåµÈ ±¤´ëÇÑ ¾çÀÇ ÈÇÕ¹°µéÀ» chemistry space »ó¿¡ Åõ¿µÇÏ¿© subsetÀ» ¼±ÅÃÇÏ´Â ¹æ¹ýÀ¸·Î ó¸®Çϴµ¥ »ç¿ëÇϰųª, ÀÚü µ¥ÀÌÅͺ£À̽º¸¦ ±¸¼ºÇϴ óÀ½ ´Ü°è¿¡¼ »ç¿ëÇÒ ¼ö ÀÖ½À´Ï´Ù. ÈÇÕ¹° ȹµæ, ¹Ýº¹À» ÅëÇÑ lead ÃßÀû, ´Ù¾ç¼ºÀ» °¡Áø library µðÀÚÀÎ, de novo µðÀÚÀÎ, ÈÇÕ¹°ÀÇ ½Ã°¢È, ÈÇÐ ¿µ¿ª Àüü¸¦ ¾Æ¿ì¸£´Â library¸¦ ¸ðµÎ Æ÷ÇÔÇÕ´Ï´Ù.
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The tight clustering of 74 ACE inhibitors (in magenta) within the three-dimensional ACE-receptor-relevant subspace of the MDDR chemistry-space. Only a 5% diverse subset of the total MDDR population is shown (in cyan). The initial 6-dimensional chemistry-space of the MDDR compounds was reduced to three dimensions by considering only those metrics relevant to the ACE receptor. |
Key Benefits
· Å« ¸ðÁý´Ü¿¡¼ ´Ù¾çÇÏ°í ´ëÇ¥¼ºÀ» °¡Áø ºÐÀÚµéÀÇ subsetÀ» ¼±ÅÃÇÔÀ¸·Î½á ÇÕ¼ºÀ̳ª °Ë»ö¿¡¼ ¾µµ¥¾ø´Â ¹Ýº¹À» ÁÙÀÌ°í ¿¬±¸¿¡ µå´Â ³ë·ÂÀ» ÁýÁß½Ãų ¼ö ÀÖ½À´Ï´Ù.
· Library¿¡ ¼ÓÇÑ ÈÇÕ¹°µé·Î ä¿öÁöÁö ¾Ê¾Æ¼ °Ë»öµÇÁö ¾Ê´Â chemistry-space ¿µ¿ªÀ» È®ÀÎÇÒ ¼ö ÀÖ½À´Ï´Ù.
· µÎ °³ ¶Ç´Â ±× ÀÌ»óÀÇ µ¥ÀÌÅͺ£À̽º ´Ù¾ç¼ºÀ» Æò°¡ÇÏ´Â °Í, ¶Ç´Â ÈÇÕ¹° ÁýÇÕÀ̳ª chemistry-space°¡ »óÈ£ º¸¿ÏÀûÀ̰ųª °ãÄ¡´Â combinatorial library¸¦ ÀÌ¿ëÇØ ÈÇÕ¹° ȹµæÀ» ÃÖÀûÈ ÇÕ´Ï´Ù.
Selector™
: Characterize and Sample Compound Libraries
Selector™´Â ÈÇÕ¹° ¼¼Æ®ÀÇ Æ¯Â¡À» ±ÔÁ¤, ºñ±³ÇÏ°í »ùÇÃÀ» ÃßÃâÇÕ´Ï´Ù. Clustering toolµéÀº similarity¿¡ ±âÃÊÇÏ¿© ÈÇÕ¹° °£ÀÇ °ü°è¸¦ È®ÀÎÇÕ´Ï´Ù. Selector´Â ´Ù¾ç¼ºÀ» °®´Â, ¶Ç´Â ´ëÇ¥¼ºÀ» °¡Áø subsetÀ» ¸¸µé ¼ö ÀÖ°í Æ¯¼º¿¡ µû¶ó ÈÇÕ¹°À» °É·¯³»¾î lead ÈÇÕ¹°°ú À¯»çÇÑ ÈÇÕ¹°µéÀ» ã¾Æ³»¸ç ÈÇÕ¹° ¼¼Æ®ÀÇ ´Ù¾ç¼ºÀ» ºñ±³ÇÒ ¼ö ÀÖ½À´Ï´Ù. °³º°ÀûÀÎ ºÐÀÚ ±¸Á¶¿¡ ´ëÇØ ¸¹Àº Ư¼ºµéÀÌ °è»êµÉ ¼ö ÀÖ°í, ÀÌ·¯ÇÑ Æ¯¼ºµéÀÇ ÁýÇÕ¿¡ ÀÇÇØ ÈÇÕ¹° libraryÀÇ ±¸Á¶Àû "space"°¡ Á¤Àǵ˴ϴÙ. Selector´Â libraryÀÇ Æ¯Â¡À» ±ÔÁ¤ÇÏ´Â ÀÌ·¯ÇÑ Æ¯¼ºµéÀ» ÀÌ¿ëÇÏ¿© diversity scaleÀ» °áÁ¤ÇÏ°í ±× Á¤º¸¿¡ ±âÃÊÇÏ¿© ÈÇÕ¹°À» ¼±ÅÃÇÒ ¼ö ÀÖµµ·Ï µµ¿ÍÁÝ´Ï´Ù.
Key Benefits
· ¼ö½Ê¸¸ °³ÀÇ ÈÇÕ¹°¿¡ Àû¿ëÇÏ´Â °ÍÀÌ °¡´ÉÇÕ´Ï´Ù.
· SYBYL¢ç Molecular Spreadsheet¢âÀ̳ª UNITY¢ç µ¥ÀÌÅͺ£À̽º¿¡¼ ¾òÀº µ¥ÀÌÅ͸¦ »ç¿ëÇÒ ¼ö ÀÖ½À´Ï´Ù.
· Molecular SpreadsheetÀÇ property descriptorµéÀ» È®ÀåÇÕ´Ï´Ù.
· ´Ù¾çÇÑ property descriptor¸¦ »ç¿ëÇÕ´Ï´Ù.
· Jarvis-Patrick, Reciprocal Nearest Neighbor, Hierarchical Clustering, Staged ClusteringÀ» Æ÷ÇÔÇÑ ´Ù¾çÇÑ ºÐ¼® toolÀ» »ç¿ëÇÕ´Ï´Ù.
· UNITY µ¥ÀÌÅͺ£À̽º¿¡ ÇÊÀûÇÏ´Â À¯¿ë¼ºÀ» °¡Áö°í ÀÖ½À´Ï´Ù.


RACHEL™
: Sophisticated Tools for Optimization of Lead Compounds
RACHELÀº ligand/receptor ±¸Á¶¿¡¼ »ç¿ëÀÚ°¡ ÁöÁ¤ÇÑ ligand site¸¦ lead ÈÇÕ¹°·Î ÇÏ¿© ÀÚµ¿ÀûÀ¸·Î combinatorial optimizationÀ» ¼öÇàÇÕ´Ï´Ù. À̶§ ÈÇÕ¹°µéÀº Ȱ¼º ÀÚ¸®¿¡¼ °¡´ÉÇÑ conformationÀ» °¡Áö°Ô µÇ¸é¼ receptor¿¡ ´Ü´ÜÈ÷ binding ÇÕ´Ï´Ù. Lead ÈÇÕ¹°ÀÇ ÀÌ·¯ÇÑ »õ·Î¿î ±¸¼ºÀº °è»êÀÇ ÁøÇà¿¡ µû¶ó ´ÙÀ½ ¼¼´ë¸¦ Çü¼ºÇÏ°Ô µÇ´Âµ¥, ¹Ýº¹ÀûÀÎ °è»êÀ» ÅëÇØ ³ôÀº ģȵµ¸¦ °®´Â ±¸Á¶µéÀÇ ¼¼Æ®·Î refineµË´Ï´Ù.
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The X-ray structure of wildtype tern N9 influenza virus neuraminidase (2QWK) shown with five ligands generated using RACHEL that are predicted to be active. Hydrogen bonds between the ligands and residues are indicated by dashed yellow lines. The surface was rendered using MOLCAD¢â and color-coded according to hydrogen acceptor/donor density. Dark purple regions contain a greater acceptor/donor density and light purple regions indicate areas where hydrogen bonding is less likely to occur. |
Key Benefits
· Chemical fragment´Â ÇÕ¼ºÀû Á¢±ÙÀÇ °¡´É¼ºÀÌ Áõ°¡Çϰí ÀÖ´Â °³ÀÎ, ±â¾÷, »ó¾÷Àû µ¥ÀÌÅͺ£À̽º·ÎºÎÅÍ ¾òÀ» ¼ö ÀÖ½À´Ï´Ù.
· BindingÀ» ¹æÇØÇÏ´Â component´Â Ȱ¼º ÀÚ¸®¿¡¼ favorable interaction¿¡ ±âÃÊÇÏ¿© ¼±Åõ˴ϴÙ.
· Focused scoring functionÀº »ç¿ëÀÚ°¡ Á¦°øÇÑ ±¸Á¶-Ȱ¼º µ¥ÀÌÅÍ¿¡ ±Ù°ÅÇÏ¿© ÀÚµ¿ÀûÀ¸·Î »ý¼ºµË´Ï´Ù.
· Template¿Í chemical descriptor´Â »ç¿ëÀÚ°¡ ±¸Á¶ »ý¼ºÀ» ¿ÏÀüÈ÷ ÅëÁ¦ÇÒ ¼ö ÀÖ´Â °¢°¢ÀÇ substitution site¿¡ ´ëÇØ µ¶¸³ÀûÀ¸·Î ±¸¼ºµÉ ¼ö ÀÖ½À´Ï´Ù.
· Conformational °Ë»ö ¿£ÁøÀº 1ÃÊ¿¡ 106°³ÀÇ conformer¸¦ sampling ÇÒ ¼ö ÀÖ½À´Ï´Ù.
EA-Inventor
: Invent New Compound Ideas and Lead Hop Using Novel de novo Design Engine
EA-Inventer´Â de novo µðÀÚÀο¡ ´ëÇÑ »õ·Ó°í Â÷º°ÈµÈ Á¢±Ù¹ýÀ» Á¦°øÇÏ´Â toolÀÔ´Ï´Ù.
EA-Inventor´Â receptor-based¿Í ligand-based µðÀÚÀο¡ ¸ðµÎ »ç¿ëµÉ ¼ö ÀÖ°í »õ·Î¿î ÈÇÕ¹°À̳ª(in silico lead discovery), °íÁ¤µÈ scaffold ÁÖÀ§ÀÇ »õ·Î¿î R-group(lead exploration), ¶Ç´Â »õ·Î¿î scaffold(lead- ¶Ç´Â scaffold-"hopping")¸¦ ¸¸µé¾î ³¾ ¼ö ÀÖ½À´Ï´Ù. ´Ù¸¥ ÀüÇüÀûÀÎ de novo µðÀÚÀÎ ÇÁ·Î±×·¥µé°ú ´Þ¸®, EA-Inventor´Â ¿øÇÏ´Â ¾î¶² scoring function(¶Ç´Â composite scoring function)ÀÌµç ºÙ¿©¼ »ç¿ëÇÒ ¼ö ÀÖ½À´Ï´Ù. Áï, EA-Inventor¿¡¼´Â »õ·Î¿î structure°¡ »ý¼ºµÇ°í scoringµÇ´Â ¹æ½ÄÀ» »ç¿ëÀÚ°¡ ¸ðµÎ °áÁ¤ÇÒ ¼ö ÀÖ½À´Ï´Ù.
EA-Inventor·Î ¸¸µé¾îÁø structure´Â º¸Åë ÇÕ¼ºÀÌ °¡´ÉÇÑ °ÍµéÀ̰í, ÇÕ¼ºÇÏÁö ¾Ê´õ¶óµµ CAMD scientist¿Í bench chemistÀÇ »ó»ó·Â¿¡ Ȱ±â¸¦ ºÒ¾î³Ö°í ±×°ÍÀ» º¸¿ÏÇØ Áִµ¥ µµ¿òÀ» ÁÙ ¼ö ÀÖÀ» °ÍÀÔ´Ï´Ù.
Key Benefits
· Evolutionary Algorithm (Genetic Algorithm°ú À¯»ç)À» »ç¿ëÇÏ¿© »ç¿ëÀÚ°¡ ¼±ÅÃÇÑ scoring function¿¡ ÃÖÀûÈ µÈ »õ·Î¿î ÈÇÕ¹°À» »ý¼ºÇÕ ´Ï´Ù.
· ÀÌ·ÐÀûÀ¸·Î, °øÅë scaffold¸¦ °¡Áö´Â leadÀÇ È®Àå, ¶Ç´Â »õ·Î¿î scaffold¸¦ ã´Â lead-hopping µî¿¡ ÀûÇÕÇÑ inverse-QSAR ¹æ¹ýÀÔ´Ï´Ù.
· ¼±È£µÇ´Â chemistry¿Í substructureµéÀ» ÀúÀåÇϰí EA-Inventor¸¦ ÀÌ¿ëÇÏ¿© ÀÌ·¯ÇÑ ÈÇÐÀû featureµéÀ» À¯ÁöÇÏ¸é¼ »õ·Î¿î ±¸Á¶µéÀ» ¸¸µé ¾î³»´Â ¾ÆÀ̵ð¾î¸¦ Á¦¾ÈÇÕ´Ï´Ù.
· ¾î¶°ÇÑ scoring function (¶Ç´Â composite scoring function)°úµµ ¼Õ½±°Ô ÇÔ²² »ç¿ëÇÒ ¼ö ÀÖ½À´Ï´Ù.
· ¸Å¿ì È¿°úÀûÀÎ BCUT-similarity scoring functionÀ» »ç¿ëÇÒ ¼ö ÀÖ½À´Ï´Ù.
· ÈÇÐ ±¸Á¶¿¡ ´ëÇÑ mutation operatorÀÇ ¿Ïº®ÇÑ Á¶ÇÕ ¼¼Æ®¸¦ Á¦°øÇÕ´Ï´Ù.
· ¸ðµç ±¸Á¶Àû mutationÀÇ »ó´ë È®·üÀ» »ç¿ëÀÚ°¡ Á¶ÀýÇÒ ¼ö ÀÖ½À´Ï´Ù.
· ÈÇÐÀûÀ¸·Î ºÒ°¡´ÉÇÑ ±¸Á¶´Â »ý¼ºÇÏÁö ¾Ê½À´Ï´Ù.
· EvolutionÀÌ ÁøÇàµÇ´Â µ¿¾È º¸Á¸µÇ¾î¾ß ÇÏ´Â substructureµéÀ» ÁöÁ¤ÇÒ ¼ö ÀÖ½À´Ï´Ù.


Surflex-Dock™
: Ligand-Receptor Docking and Virtual Screening. Fewer False Positives, More True Hits.
Surflex-Dock™Àº °¡»ó Ãʰí¼Ó °Ë»ö(vHTS)¿¡¼ °á°ú¹°ÀÇ ÁúÀû Çâ»ó°ú ÇÔ²² ¸Å¿ì ºü¸¥ ¼Óµµ¿Í Á¤È®¼º, À¯¿ë¼ºÀ» ¸ðµÎ Á¦°øÇÕ´Ï´Ù. ÀÌ toolÀº Ãß°¡ÀûÀÎ negative training µ¥ÀÌÅÍ, surface-based molecular similarity method¿¡ ±â¹ÝÇÑ °Ë»ö ¿£Áø°ú ÇÔ²² ÃÖ½ÅÀÇ re-parameterize µÈ °æÇèÀûÀÎ scoring function(Hammerhead docking system¿¡ ±â¹Ý)À» »ç¿ëÇÕ´Ï´Ù.
Surflex-DockÀº ¿¬±¸Àڵ鿡 ÀÇÇØ ±¤¹üÀ§ÇÏ°Ô °ËÁõµÇ¾î ¿ÔÀ¸¸ç, ÀÌÀüÀÇ ¸ðµç °æÀï method¿Í ºñ±³ÇßÀ» ¶§ ÁÁÀº Æò°¡¸¦ ¹Þ°í ÀÖ½À´Ï´Ù. (Kellenberger et al., (2004) Proteins: Structure, Function, and Bioinformatics 57, 225-242, along with 28 other studies by the applications author, Prof. Ajay N. Jain, Ph.D., a faculty member at the University of California San Francisco Cancer Research Institute.)
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Influenza virus neuraminidase (1B9V) in complex with an inhibitor (purple capped sticks). The minimized inhibitor has been redocked by Surflex-Dock into the protein (yellow capped sticks) with an rms deviation of 0.645 Angstroms. |
Key Benefits
· False positive binding score¸¦ ÁÙÀ̱â À§ÇØ, ÀÌ¹Ì ¾Ë°í ÀÖ´Â binding affinity µ¥ÀÌÅÍ¿Í negative training µ¥ÀÌÅͷκÎÅÍ scoring function À» À¯µµÇÏ¿© Á¤È®ÇÑ score¸¦ ¾òÀ» ¼ö ÀÖ½À´Ï´Ù.
· Æò±ÕÀûÀ¸·Î ¸®°£µå ´ç 17Ãʰ¡ ¼Ò¿äµË´Ï´Ù.(°¢ rotatable bond ´ç ~3ÃÊ)
· Default ¼¼ÆÃÀ¸·Îµµ ÁÁÀº docking °á°ú¸¦ ¾òÀ» ¼ö ÀÖ¾î, docking preparationÀÌ ½¬¿öÁý´Ï´Ù.
· DockingÀÇ °¡À̵尡 µÇ´Â ProtomolÀ» ÀÚµ¿ÀûÀ¸·Î, ¶Ç´Â »ç¿ëÀÚ ÁöÁ¤À¸·Î »ý¼ºÇÒ ¼ö ÀÖ½À´Ï´Ù.
· °¢°¢ÀÇ flexible ring system¿¡ generic ring conformationÀÌ Àû¿ëµÇ¾î conformationÀÌ minimize µË´Ï´Ù.
· ´ÙÁß ¿¬»êÀ» °¡´ÉÇϵµ·Ï ÇÏ´Â °£´ÜÇÑ parallelization ÇÁ·ÎÅäÄÝÀ» Áö¿øÇÕ´Ï´Ù.


UNITY¢ç
: Locate Compounds in Databases that Match a Pharmacophore or Fit a Receptor Site
UnityÀÇ »ç¿ë ¸ñÀûÀº »õ·Î¿î lead ÈÇÕ¹°À» ºü¸¥ ½Ã°£ ¾È¿¡ ã¾Æ³»´Âµ¥ ÀÖÀ¸¸ç, Á¤È®ÇÑ ÀԷ°ªÀ» ÁÖ¾î Èĺ¸ ¹°ÁúÀÇ ¼Õ½ÇÀ» ÃÖ¼ÒÈÇÏ¸é¼ hit list¸¦ ÁÙ¿©³ª°¡´Â °ÍÀÔ´Ï´Ù. Unity¿¡¼´Â queryÀÇ Á¾·ù¿¡ µû¶ó °Ë»ö ±âÁØ ¼³Á¤À» ´Ù¾çÇÏ°Ô º¯°æÇÒ ¼ö ÀÖÀ¸¹Ç·Î »ç¿ëÀÚÀÇ ¿¬±¸ ¸ñÀû¿¡ µû¶ó queryÀÇ Á¾·ù¸¦ ¼±ÅÃÇÒ ¼ö ÀÖ½À´Ï´Ù. ¶ÇÇÑ target ºÐÀÚ°¡ ¼öÁ¤µÈ Á¤µµ¿Í constrain, ±×¸®°í receptor¸¦ Á¤ÀÇÇÏ´Â ¹æ½Ä¿¡ µû¶ó hit listÀÇ ¼ö¸¦ Á¶ÀýÇÒ ¼ö ÀÖ½À´Ï´Ù.
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A UNITY query constructed at the active site of the streptavidin/biotin complex (1STP). Yellow lines originate at hydrogen bonding sites of the protein (shown as spheres) and terminate within the spatial constraint for complementary ligand sites. The rotameric positions of hydroxyl and amine H-bond donors are shown as toroidal constraints. A surface constraint at the protein/ligand interface is shown in green. The spatial cap in red accounts for a bifurcated interaction with an Asp carboxyl. Partial match groups are shown in different colors: red, yellow, or green. |
Key Benefits
· Receptor site¿¡ ÀûÇÕÇÑ ¸®°£µå¸¦ ã±â À§ÇÑ ÈÇÕ¹° µ¥ÀÌÅͺ£À̽ºÀÇ flexible search°¡ °¡´ÉÇÕ´Ï´Ù.
· Pharmacophore hypothesis¿¡ ºÎÇÕÇÏ´Â ÈÇÕ¹°À» ã±â À§ÇÑ µ¥ÀÌÅͺ£À̽º Ž»öÀÌ °¡´ÉÇÕ´Ï´Ù.
· Lead ÈÇÕ¹° ¹ß°ßÀ» À§ÇÑ ÈÇÕ¹° µ¥ÀÌÅͺ£À̽ºÀÇ virtual screenÀÌ °¡´ÉÇÕ´Ï´Ù.
· Commercial µ¥ÀÌÅͺ£À̽º¿¡¼ combinatorial synthesis¸¦ µµ¿ÍÁÙ reagentµéÀ» °áÁ¤ÇÒ ¼ö ÀÖ½À´Ï´Ù.
· ÀúÀåµÈ ±¸Á¶µéÀ» ÀÌ¿ëÇØ µ¥ÀÌÅͺ£À̽º¸¦ »ý¼ºÇÒ ¼ö ÀÖ½À´Ï´Ù.
· Combinatorial libraryÀÇ ÀúÀå ¹× °Ë»öÀÌ °¡´ÉÇÕ´Ï´Ù.
· Conformationally flexible 3D search¸¦ ºü¸£°Ô ¼öÇàÇÒ ¼ö ÀÖ½À´Ï´Ù.
· 3D query ±¸¼º¿¡ ´Ù¾çÇÑ feature¿Í constraintÀÇ ¼¼Æ®¸¦ »ç¿ëÇÒ ¼ö ÀÖ½À´Ï´Ù.
· ¸ðµç 3D search¿¡¼ query featureÀÇ partial matchingÀÌ °¡´ÉÇÕ´Ï´Ù.
· Markush capability·Î ´Ù¾çÇÑ query ÁöÁ¤ÀÌ °¡´ÉÇÕ´Ï´Ù.
· 2D/3D search filter·Î fingerprint¸¦ »ç¿ëÇÕ´Ï´Ù.
· Query´Â ½ÇÁ¦ÀûÀÎ hydrogen bond donor/acceptor site representationÀ» Æ÷ÇÔÇÕ´Ï´Ù.
· FingerprintÀÇ »ç¿ëÀÚ ¼³Á¤°ú ÆíÁýÀÌ °¡´ÉÇÕ´Ï´Ù.
· ÈÇÕ¹° ±¸Á¶ÀÇ 3D coordinate, fingerprint, ¿¬°üµÈ µ¥ÀÌÅͰ¡ ÇÔ²² ÀúÀåµË´Ï´Ù.
· °£´ÜÇÑ combinatorial library storage´Â combinatorial SLNÀ¸·Î ¸¸µé ¼ö ÀÖ½À´Ï´Ù.
· UNITY 3D µ¥ÀÌÅͺ£À̽º´Â CONCORD¿Í SteroPlex¸¦ »ç¿ëÇÏ¿© 2D µ¥ÀÌÅͺ£À̽º·ÎºÎÅÍ ¸¸µé ¼ö ÀÖ½À´Ï´Ù.
· UNITY °á°ú´Â query¿¡ alignµÈ °ÍÀ¸·Î ÁÖ¾îÁö¹Ç·Î QSAR with CoMFA¿¡¼ »ç¿ëÀÌ °¡´ÉÇÕ´Ï´Ù.
· Receptor-site query·ÎºÎÅÍ ³ª¿Â hit´Â receptor¿¡ docking ÇÒ ¼ö ÀÖ½À´Ï´Ù.
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