48:, toxicity, environmental hazards and much more, which are associated with well-defined structural modifications. Single point changes in the molecule pairs are termed a chemical transformation or Molecular transformation. Each molecular pair is associated with a particular transformation. An example of transformation is the replacement of one functional group by another. More specifically, molecular transformation can be defined as the replacement of a molecular fragment having one, two or three attachment points with another fragment. Useful Molecular transformation in a specified context is termed as "Significant" transformations. For example, a transformation may systematically decrease or increase a desired property of chemical compounds. Transformations that affect a particular property/activity in a statistically significant sense are called as significant transformations. The transformation is considered significant, if it increases the property value "more often" than it decreases it or vice versa. Thus, the distribution of increasing and decreasing pairs should be significantly different from the binomial ("no effect") distribution with a particular p-value (usually 0.05).
90:
of its activity? Or if it is predicted to be inactive, how its activity can be modulated? The black box nature of the QSAR model prevents it from addressing these crucial issues. The use of predicted MMPs allows to interpret models and identify which MMPs were learned by the model. The MMPs, which were not reproduced by the model, could correspond to experimental errors or deficiency of the model (inappropriate descriptors, too few data, etc.).
52:
150:
The application of the MMPA across large chemical databases for the optimization of ligand potency is problematic because same structural transformation may increase or decrease or doesn't affect the potency of different compounds in the dataset. Selection of practical significant transformation from
27:
that compares the properties of two molecules that differ only by a single chemical transformation, such as the substitution of a hydrogen atom by a chlorine one. Such pairs of compounds are known as matched molecular pairs (MMP). Because the structural difference between the two molecules is small,
154:
Beside these, MMPA might pose some limitations in terms of computational resources, especially when dealing with databases of compounds with a large number of breakable bonds. Further, more atoms in the variable part of the molecule also leads to combinatorial explosion problems. To deal with this,
89:
are similar to "black boxes", which provide predictions that can't be easily interpreted. This problem undermines the applicability of QSAR model in helping the medicinal chemist to make the decision. If the compound is predicted to be active against some microorganism, what are the driving factors
141:
Here instead of looking at the pair of molecules which differ only at one point, a series of more than 2 molecules different at a single point is considered. The concept of matching molecular series was introduced by Wawer and
Bajorath. It is argued that longer matched series is more likely to
103:
relationship (SAR). This discontinuity also indicates high SAR information content, because small chemical changes in the set of similar compounds lead to large changes in activity. The assessment of activity cliffs requires careful consideration of similarity and potency difference criteria.
102:
Activity cliffs are pairs or groups of compounds that are highly similar in the structures but have large different in potency towards the same target. Activity cliffs received great attention in computational chemistry and drug discovery as they represent a discontinuity in structure-activity
132:
algorithm is used to finds all possible matched pairs in a data set according to a set of predefined rules. This results in much larger numbers of matched pairs and unique transformations, which are typically filtered during the process to identify those transformations that correspond to
64:
MMP based analysis is an attractive method for computational analysis because they can be algorithmically generated and they make it possible to associate defined structural modifications at the level of compound pairs with chemical property changes, including biological activity.
93:
Analysis of MMPs (matched molecular pair) can be very useful for understanding the mechanism of action. A medicinal chemist might be interested particularly in "activity cliff". Activity cliff is a minor structural modification, which changes the target activity significantly.
77:(QSAR) modelling studies. One of the issues of QSAR models is they are difficult to interpret in a chemically meaningful manner. While it can be pretty easy to interpret simple linear regression models, the most powerful
119:
In supervised MMPA, the chemical transformations are predefined, then the corresponding matched pair compounds are found within the data set and the change in end point computed for each transformation.
28:
any experimentally observed change in a physical or biological property between the matched molecular pair can more easily be interpreted. The term was first coined by Kenny and
Sadowski in the book
151:
a dataset of molecules is a challenging issue in the MMPA. Moreover, the effect of a particular molecular transformation can significantly depend on the
Chemical context of transformations.
74:
596:
Warner, D. J.; Bridgland-Taylor, M. H.; Sefton, C. E.; Wood, D. J. (2012). "Prospective prediction of antitarget activity by matched molecular pairs analysis".
640:
477:
Stumpfe D, Hu Y, Dimova D, et al.: Recent progress in understanding activity cliffs and their utility in medicinal chemistry. J Med Chem. 2014; 57(1): 18–28.
40:
MMP can be defined as a pair of molecules that differ in only a minor single point change (See Fig 1). Matched molecular pairs (MMPs) are widely used in
510:
Wawer, Mathias; Bajorath, Jürgen (2011). "Local
Structural Changes, Global Data Views: Graphical Substructure−Activity Relationship Trailing".
230:
Wassermann, A.M.; Dimova, D.; Iyer P; et al. (2012). "Advances in computational medicinal chemistry: matched molecular pair analysis".
194:
Griffen, Ed; Leach, Andrew G.; Robb, Graeme R.; Warner, Daniel J. (2011). "Matched molecular pairs as a medicinal chemistry tool".
171:
Kenny, Peter W.; Sadowski, Jens (2005). "Chapter 11: Structure
Modification in Chemical Databases". In Oprea, Tudor I. (ed.).
265:
Dossetter, Alexander G.; Griffen, Edward J.; Leach, Andrew G. (2013). "Matched molecular pair analysis in drug discovery".
351:
Sushko, Yurii; Novotarskyi, Sergii; Körner, Robert; Vogt, Joachim; Abdelaziz, Ahmed; Tetko, Igor V (2014-12-11).
684:
155:
the number of breakable bonds and number of atoms in the variable part can be used to pre-filter the database.
461:
Stumpfe D, Bajorath J: Exploring activity cliffs in medicinal chemistry. J Med Chem. 2012; 55(7): 2932–2942
111:
Matched molecular pair (MMPA) analyses can be classified into two types: supervised and unsupervised MMPA.
689:
86:
353:"Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process"
133:
statistically significant changes in the targeted property with a reasonable number of matched pairs.
267:
45:
41:
621:
333:
247:
142:
exhibit preferred molecular transformation while, matched pairs exhibit only a small preference.
661:
613:
578:
528:
478:
462:
444:
426:
384:
325:
284:
212:
653:
605:
568:
560:
520:
494:
493:
Hu Y, Stumpfe D, Bajorath J: Advancing the activity cliff concept . F1000Res. 2013; 2: 199.
434:
418:
374:
364:
315:
276:
239:
204:
180:
129:
24:
641:"Statistical analysis of the effects of common chemical substituents on ligand potency"
573:
547:
439:
406:
379:
352:
82:
678:
645:
552:
548:"Using Matched Molecular Series as a Predictive Tool To Optimize Biological Activity"
512:
196:
498:
251:
625:
337:
173:
280:
369:
430:
422:
78:
665:
617:
609:
582:
532:
482:
466:
448:
388:
329:
288:
216:
546:
O'Boyle, Noel M.; Boström, Jonas; Sayle, Roger A.; Gill, Adrian (2014).
657:
564:
524:
243:
208:
320:
303:
50:
51:
405:
Stumpfe, Dagmar; Hu, Huabin; Bajorath, Jürgen (2019-09-10).
304:"Chemical predictive modelling to improve compound quality"
55:
Fig 1: Exemplary MMPs (differences highlighted in orange):
44:
to study changes in compound properties which includes
172:
179:. Wiley-VCH Verlag GmbH & Co. KGaA. pp.
8:
75:quantitative structure–activity relationship
572:
438:
378:
368:
319:
163:
407:"Evolving Concept of Activity Cliffs"
73:MMPA is quite useful in the field of
7:
400:
398:
639:Hajduk, P.J.; Sauer, D.R. (2008).
175:Chemoinformatics in Drug Discovery
60:Significance of MMP based analysis
30:Chemoinformatics in Drug Discovery
14:
302:Cumming, J.; et al. (2013).
128:Also known as automated MMPAs. A
499:10.12688/f1000research.2-199.v1
17:Matched molecular pair analysis
1:
308:Nature Reviews Drug Discovery
281:10.1016/j.drudis.2013.03.003
107:Types of MMP based analysis
706:
357:Journal of Cheminformatics
370:10.1186/s13321-014-0048-0
232:Drug Development Research
69:Interpretable QSAR models
423:10.1021/acsomega.9b02221
137:Matched molecular series
610:10.1002/minf.201200020
87:support vector machine
56:
54:
268:Drug Discovery Today
417:(11): 14360–14368.
46:biological activity
42:medicinal chemistry
275:(15–16): 724–731.
57:
658:10.1021/jm070838y
565:10.1021/jm500022q
525:10.1021/jm200026b
244:10.1002/ddr.21045
209:10.1021/jm200452d
124:Unsupervised MMPA
23:) is a method in
697:
670:
669:
636:
630:
629:
593:
587:
586:
576:
559:(6): 2704–2713.
543:
537:
536:
519:(8): 2944–2951.
507:
501:
491:
485:
475:
469:
459:
453:
452:
442:
402:
393:
392:
382:
372:
348:
342:
341:
323:
299:
293:
292:
262:
256:
255:
227:
221:
220:
191:
185:
184:
178:
168:
130:machine learning
705:
704:
700:
699:
698:
696:
695:
694:
685:Cheminformatics
675:
674:
673:
638:
637:
633:
595:
594:
590:
545:
544:
540:
509:
508:
504:
492:
488:
476:
472:
460:
456:
404:
403:
396:
350:
349:
345:
321:10.1038/nrd4128
314:(12): 948–962.
301:
300:
296:
264:
263:
259:
229:
228:
224:
203:(22): 7739–50.
193:
192:
188:
170:
169:
165:
161:
148:
139:
126:
117:
115:Supervised MMPA
109:
100:
83:neural networks
71:
62:
38:
25:cheminformatics
12:
11:
5:
703:
701:
693:
692:
687:
677:
676:
672:
671:
631:
604:(5): 365–368.
588:
538:
502:
486:
470:
454:
394:
343:
294:
257:
238:(8): 518–527.
222:
186:
162:
160:
157:
147:
144:
138:
135:
125:
122:
116:
113:
108:
105:
99:
98:Activity Cliff
96:
70:
67:
61:
58:
37:
34:
13:
10:
9:
6:
4:
3:
2:
702:
691:
690:Biostatistics
688:
686:
683:
682:
680:
667:
663:
659:
655:
652:(3): 553–64.
651:
648:
647:
646:J. Med. Chem.
642:
635:
632:
627:
623:
619:
615:
611:
607:
603:
599:
592:
589:
584:
580:
575:
570:
566:
562:
558:
555:
554:
553:J. Med. Chem.
549:
542:
539:
534:
530:
526:
522:
518:
515:
514:
513:J. Med. Chem.
506:
503:
500:
496:
490:
487:
484:
480:
474:
471:
468:
464:
458:
455:
450:
446:
441:
436:
432:
428:
424:
420:
416:
412:
408:
401:
399:
395:
390:
386:
381:
376:
371:
366:
362:
358:
354:
347:
344:
339:
335:
331:
327:
322:
317:
313:
309:
305:
298:
295:
290:
286:
282:
278:
274:
270:
269:
261:
258:
253:
249:
245:
241:
237:
233:
226:
223:
218:
214:
210:
206:
202:
199:
198:
197:J. Med. Chem.
190:
187:
182:
177:
176:
167:
164:
158:
156:
152:
145:
143:
136:
134:
131:
123:
121:
114:
112:
106:
104:
97:
95:
91:
88:
84:
80:
76:
68:
66:
59:
53:
49:
47:
43:
35:
33:
31:
26:
22:
18:
649:
644:
634:
601:
597:
591:
556:
551:
541:
516:
511:
505:
489:
473:
457:
414:
410:
360:
356:
346:
311:
307:
297:
272:
266:
260:
235:
231:
225:
200:
195:
189:
174:
166:
153:
149:
140:
127:
118:
110:
101:
92:
72:
63:
39:
36:Introduction
29:
20:
16:
15:
598:Mol. Inform
146:Limitations
679:Categories
159:References
79:algorithms
431:2470-1343
411:ACS Omega
363:(1): 48.
666:18173228
618:27477265
583:24601597
533:21443196
483:23981118
467:22236250
449:31528788
389:25544551
330:24287782
289:23557664
252:82321850
217:21936582
626:5430494
574:3968889
440:6740043
380:4272757
338:6218976
181:271–285
664:
624:
616:
581:
571:
531:
481:
465:
447:
437:
429:
387:
377:
336:
328:
287:
250:
215:
622:S2CID
334:S2CID
248:S2CID
81:like
662:PMID
614:PMID
579:PMID
529:PMID
479:PMID
463:PMID
445:PMID
427:ISSN
385:PMID
326:PMID
285:PMID
213:PMID
21:MMPA
654:doi
606:doi
569:PMC
561:doi
521:doi
495:doi
435:PMC
419:doi
375:PMC
365:doi
316:doi
277:doi
240:doi
205:doi
681::
660:.
650:51
643:.
620:.
612:.
602:31
600:.
577:.
567:.
557:57
550:.
527:.
517:54
443:.
433:.
425:.
413:.
409:.
397:^
383:.
373:.
359:.
355:.
332:.
324:.
312:12
310:.
306:.
283:.
273:18
271:.
246:.
236:73
234:.
211:.
201:54
85:,
32:.
668:.
656::
628:.
608::
585:.
563::
535:.
523::
497::
451:.
421::
415:4
391:.
367::
361:6
340:.
318::
291:.
279::
254:.
242::
219:.
207::
183:.
19:(
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
