Antonio de la Vega de León

Matched molecular pairs derived by retrosynthetic fragmentation

Matched molecular pairs (MMPs) are defined as pairs of compounds that only differ by a chemical change at a single site. MMPs have become popular in medicinal chemistry to support lead optimization, absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis, and other applications. Thus far, MMPs have been algorithmically defined and not on the basis of reaction information. This often limits the chemical interpretability and practical utility of MMPs. Therefore, we introduce synthetically accessible MMPs that are automatically generated by applying reaction rules following the retrosynthetic combinatorial analysis procedure (RECAP). A library of more than 92 000 RECAP-MMPs was generated from public domain compounds active against 435 different targets exclusively utilizing high-confidence activity data. This library is made freely available for use in medicinal chemistry.

Synthesis, biological evaluation and molecular docking of N-phenyl thiosemicarbazones as urease inhibitors.

Urease is an important enzyme which breaks urea into ammonia and carbon dioxide during metabolic processes. However, an elevated activity of urease causes various complications of clinical importance. The inhibition of urease activity with small molecules as inhibitors is an effective strategy for therapeutic intervention. Herein, we have synthesized a series of 19 benzofurane linked N-phenyl semithiocarbazones (3a-3s). All the compounds were screened for enzyme inhibitor activity against Jack bean urease. The synthesized N-phenyl thiosemicarbazones had varying activity levels with IC50 values between 0.077 ± 0.001 and 24.04 ± 0.14 μM compared to standard inhibitor, thiourea (IC50 = 21 ± 0.11 μM). The activities of these compounds may be due to their close resemblance of thiourea. A docking study with Jack bean urease (PDB ID: 4H9M) revealed possible binding modes of N-phenyl thiosemicarbazones.

Advancing the activity cliff concept, part II

We present a follow up contribution to further complement a previous commentary on the activity cliff concept and recent advances in activity cliff research. Activity cliffs have originally been defined as pairs of structurally similar compounds that display a large difference in potency against a given target. For medicinal chemistry, activity cliffs are of high interest because structure-activity relationship (SAR) determinants can often be deduced from them. Herein, we present up-to-date results of systematic analyses of the ligand efficiency and lipophilic efficiency relationships between activity cliff-forming compounds, which further increase their attractiveness for the practice of medicinal chemistry. In addition, we summarize the results of a new analysis of coordinated activity cliffs and clusters they form. Taken together, these findings considerably add to our evaluation and current understanding of the activity cliff concept. The results should be viewed in light of the previous commentary article.

Monitoring the Progression of Structure–Activity Relationship Information during Lead Optimization

Lead optimization (LO) in medicinal chemistry is largely driven by hypotheses and depends on the ingenuity, experience, and intuition of medicinal chemists, focusing on the key question of which compound should be made next. It is essentially impossible to predict whether an LO project might ultimately be successful, and it is also very difficult to estimate when a sufficient number of compounds has been evaluated to judge the odds of a project. Given the subjective nature of LO decisions and the inherent optimism of project teams, very few attempts have been made to systematically evaluate project progression. Herein, we introduce a computational framework to follow the evolution of structure-activity relationship (SAR) information over a time course. The approach is based on the use of SAR matrix data structures as a diagnostic tool and enables graphical analysis of SAR redundancy and project progression. This framework should help the process of making decisions in close-in analogue work.

Prediction of Compound Potency Changes in Matched Molecular Pairs Using Support Vector Regression

Matched molecular pairs (MMPs) consist of pairs of compounds that are transformed into each other by a substructure exchange. If MMPs are formed by compounds sharing the same biological activity, they encode a potency change. If the potency difference between MMP compounds is very small, the substructure exchange (chemical transformation) encodes a bioisosteric replacement; if the difference is very large, the transformation encodes an activity cliff. For a given compound activity class, MMPs comprehensively capture existing structural relationships and represent a spectrum of potency changes for structurally analogous compounds. We have aimed to predict potency changes encoded by MMPs. This prediction task principally differs from conventional quantitative structure-activity relationship (QSAR) analysis. For the prediction of MMP-associated potency changes, we introduce direction-dependent MMPs and combine MMP analysis with support vector regression (SVR) modeling. Combinations of newly designed kernel functions and fingerprint descriptors are explored. The resulting SVR models yield accurate predictions of MMP-encoded potency changes for many different data sets. Shared key structure context is found to contribute critically to prediction accuracy. SVR models reach higher performance than random forest (RF) and MMP-based averaging calculations carried out as controls. A comparison of SVR with kernel ridge regression indicates that prediction accuracy has largely been a consequence of kernel characteristics rather than SVR optimization details.

Draft Genome Sequence of the Steroid Degrader Rhodococcus ruber Strain Chol-4

ABSTRACT The whole-genome shotgun sequence of Rhodococcus ruber strain Chol-4 is presented here. This organism was shown to be able to grow using many steroids as the sole carbon and energy sources. These sequence data will help us to further explore the metabolic abilities of this versatile degrader.

Chemical space visualization: transforming multidimensional chemical spaces into similarity-based molecular networks

BACKGROUND The concept of chemical space is of fundamental relevance for medicinal chemistry and chemical informatics. Multidimensional chemical space representations are coordinate-based. Chemical space networks (CSNs) have been introduced as a coordinate-free representation. RESULTS A computational approach is presented for the transformation of multidimensional chemical space into CSNs. The design of transformation CSNs (TRANS-CSNs) is based upon a similarity function that directly reflects distance relationships in original multidimensional space. CONCLUSION TRANS-CSNs provide an immediate visualization of coordinate-based chemical space and do not require the use of dimensionality reduction techniques. At low network density, TRANS-CSNs are readily interpretable and make it possible to evaluate structure-activity relationship information originating from multidimensional chemical space.

Systematic Identification of Matching Molecular Series and Mapping of Screening Hits

Matching molecular series (MMS) have originally been introduced as an extension of the matched molecular pair (MMP) concept to facilitate the design of substructure‐based structure‐activity relationship (SAR) networks. An MMP is defined as a pair of compounds that only differ by a structural change at a single site. In addition, an MMS is defined as an MMP‐based series of compounds that have a conserved structural core and are distinguished by modifications at a single site. Systematic generation of MMS from specifically active compounds generalizes the search for series of structural analogs. Potency‐ordered MMS provide series associated with SAR information. We have systematically extracted MMS from publicly available compounds with well‐defined activity measurements and generated a large database with approx. 40 000 single‐ and 13 600 multi‐target series, which provide a rich source of SAR information. As an application, we introduce MMP‐based mapping of screening hits to MMS to search for initial SAR information and determine all SAR environments available for such hits. The MMS database is made freely available to the scientific community.

Design of a three-dimensional multitarget activity landscape.

The design of activity landscape representations is challenging when compounds are active against multiple targets. Going beyond three or four targets, the complexity of underlying activity spaces is difficult to capture in conventional activity landscape views. Previous attempts to generate multitarget activity landscapes have predominantly utilized extensions of molecular network representations or plots of activity versus chemical similarity for pairs of active compounds. Herein, we introduce a three-dimensional multitarget activity landscape design that is based upon principles of radial coordinate visualization. Circular representations of multitarget activity and chemical reference space are combined to generate a spherical view into which compound sets are projected for interactive analysis. Interpretation of landscape content is facilitated by following three canonical views of activity, chemical, and combined activity/chemical space, respectively. These views focus on different planes of the underlying coordinate system. From the activity and combined views, compounds with well-defined target selectivity and structure-activity profile relationships can be extracted. In the activity landscape, such compounds display characteristic spatial arrangements and target activity patterns.

Compound optimization through data set-dependent chemical transformations.

We have searched for chemical transformations that improve drug development-relevant properties within a given class of active compounds, regardless of the compounds they are applied to. For different compound data sets, varying numbers of frequently occurring data set-dependent transformations were identified that consistently induced favorable changes of selected molecular properties. Sequences of compound pairs representing such transformations were determined that formed pathways leading from unfavorable to favorable regions of property space. Data set-dependent transformations were then applied to predict a series of compounds with increasingly favorable property values. By database searching the desired biological activity was detected for several designed molecules or compounds that were very similar to these molecules. Taken together our findings indicate that data set-dependent transformations can be applied to predict compounds that map to favorable regions of molecular property space and retain their biological activity.