Identification and Analysis of Activity Cliffs Using 3D Similarity Techniques

<p>The analysis of activity landscapes and activity cliffs is a widely used method to locate critical regions of SAR. Knowledge of what changes in a series of molecules caused unexpectedly large changes in affinity allows the chemist to focus on the molecular features which are crucial for activity. We examine the usefulness of activity cliff analysis with a metric based on 3D shape and electrostatic similarity, utilizing a ligand-based alignment method. We demonstrate that 3D activity cliff analysis is complementary to the more usual 2D fingerprint-based methods, in that each finds cliffs that the other misses. Moreover, we show that analysis of the activity landscape in the context of a consensus 3D alignment allows the source of the activity cliff to be investigated in terms of the effect that a structural change has on the steric and electrostatic properties of a molecule. The technique is illustrated with two set of compounds with activity against acetylcholinesterase and dipeptidyl peptidase.</p>

Identification and Analysis of Activity Cliffs Using 3D Similarity Techniques

<p>The analysis of activity landscapes and activity cliffs is a widely used method to locate critical regions of SAR. Knowledge of what changes in a series of molecules caused unexpectedly large changes in affinity allows the chemist to focus on the molecular features which are crucial for activity. We examine the usefulness of activity cliff analysis with a metric based on 3D shape and electrostatic similarity, utilizing a ligand-based alignment method. We demonstrate that 3D activity cliff analysis is complementary to the more usual 2D fingerprint-based methods, in that each finds cliffs that the other misses. Moreover, we show that analysis of the activity landscape in the context of a consensus 3D alignment allows the source of the activity cliff to be investigated in terms of the effect that a structural change has on the steric and electrostatic properties of a molecule. The technique is illustrated with two set of compounds with activity against acetylcholinesterase and dipeptidyl peptidase.</p>

Lapatinib, Nilotinib and Lomitapide Inhibit Haemozoin Formation in Malaria Parasites

With the continued loss of antimalarials to resistance, drug repositioning may have a role in maximising efficiency and accelerating the discovery of new antimalarial drugs. Bayesian statistics was previously used as a tool to virtually screen USFDA approved drugs for predicted β-haematin (synthetic haemozoin) inhibition and in vitro antimalarial activity. Here, we report the experimental evaluation of nine of the highest ranked drugs, confirming the accuracy of the model by showing an overall 93% hit rate. Lapatinib, nilotinib, and lomitapide showed the best activity for inhibition of β-haematin formation and parasite growth and were found to inhibit haemozoin formation in the parasite, providing mechanistic insights into their mode of antimalarial action. We then screened the USFDA approved drugs for binding to the β-haematin crystal, applying a docking method in order to evaluate its performance. The docking method correctly identified imatinib, lapatinib, nilotinib, and lomitapide. Experimental evaluation of 22 of the highest ranked purchasable drugs showed a 24% hit rate. Lapatinib and nilotinib were chosen as templates for shape and electrostatic similarity screening for lead hopping using the in-stock ChemDiv compound catalogue. The actives were novel structures worthy of future investigation. This study presents a comparison of different in silico methods to identify new haemozoin-inhibiting chemotherapeutic alternatives for malaria that proved to be useful in different ways when taking into consideration their strengths and limitations.

Optimizing Electrostatic Similarity for Virtual Screening: A New Methodology

<div>Ligand Based Virtual Screening (LBVS) methods are widely used in drug discovery as filters for subsequent in-vitro and in-vivo characterization. This means, increasing accuracy of LBVS approaches may have a huge impact on increasing chances of success. Since the databases processed in drug discovery campaigns are enormously large, this pre-selection process requires the use of fast and precise methodologies. The similarity between compounds can be measured using different descriptors such as shape, pharmacophore or electrostatic similarity. The latter is the goal of this work, i.e., we want to improve the process of obtaining the compounds most similar to a query in terms of electrostatic similarity. To do so, the current and widely proposed methodology in the literature is based on the use of ROCS to assess the similarity of compounds in terms of shape and then evaluate a small subset of them with ZAP for prioritization regarding electrostatic similarity. This paper proposes an alternative methodology that consists of directly optimizing electrostatic similarity and works with the entire database of compounds without using shape cut-offs. For this purpose, a new and improved version of the OptiPharm software has been developed. OptiPharm implements a parameterizable metaheuristic algorithm able to solve any optimization problems directly related to the involved molecular conformations. We show that our new method completely outperforms the classical proposal widely used in the literature. Accordingly, we are able to conclude that many of the compounds proposed with our novel approach could not be discovered with the classical one. As a result, this methodology opens up new horizons in Drug Discovery.</div>

Optimizing Electrostatic Similarity for Virtual Screening: A New Methodology

<div>Ligand Based Virtual Screening (LBVS) methods are widely used in drug discovery as filters for subsequent in-vitro and in-vivo characterization. This means, increasing accuracy of LBVS approaches may have a huge impact on increasing chances of success. Since the databases processed in drug discovery campaigns are enormously large, this pre-selection process requires the use of fast and precise methodologies. The similarity between compounds can be measured using different descriptors such as shape, pharmacophore or electrostatic similarity. The latter is the goal of this work, i.e., we want to improve the process of obtaining the compounds most similar to a query in terms of electrostatic similarity. To do so, the current and widely proposed methodology in the literature is based on the use of ROCS to assess the similarity of compounds in terms of shape and then evaluate a small subset of them with ZAP for prioritization regarding electrostatic similarity. This paper proposes an alternative methodology that consists of directly optimizing electrostatic similarity and works with the entire database of compounds without using shape cut-offs. For this purpose, a new and improved version of the OptiPharm software has been developed. OptiPharm implements a parameterizable metaheuristic algorithm able to solve any optimization problems directly related to the involved molecular conformations. We show that our new method completely outperforms the classical proposal widely used in the literature. Accordingly, we are able to conclude that many of the compounds proposed with our novel approach could not be discovered with the classical one. As a result, this methodology opens up new horizons in Drug Discovery.</div>

Identification of novel JMJD2A inhibitor scaffold using shape and electrostatic similarity search combined with docking method and MM-GBSA approach

We present a hierarchical workflow combining shape- and electrostatic-based virtual screening for the identification of novel Jumonji domain-containing protein 2A (JMJD2A) inhibitors.