CACHE Challenge #1: Targeting the WDR Domain of LRRK2, A Parkinson's Disease Associated Protein.

The CACHE challenges are a series of prospective benchmarking exercises to evaluate progress in the field of computational hit-finding. Here we report the results of the inaugural CACHE challenge in which 23 computational teams each selected up to 100 commercially available compounds that they predicted would bind to the WDR domain of the Parkinson's disease target LRRK2, a domain with no known ligand and only an apo structure in the PDB. The lack of known binding data and presumably low druggability of the target is a challenge to computational hit finding methods.

Enhanced Calculation of Property Distributions in Chemical Fragment Spaces.

Chemical fragment spaces exceed traditional virtual compound libraries by orders of magnitude, making them ideal search spaces for drug design projects. However, due to their immense size, they are not compatible with traditional analysis and search algorithms that rely on the enumeration of molecules. In this paper, we present SpaceProp2, an evolution of the SpaceProp algorithm, which enables the calculation of exact property distributions for chemical fragment spaces without enumerating them.

Target 2035 - an update on private sector contributions.

Target 2035, an international federation of biomedical scientists from the public and private sectors, is leveraging 'open' principles to develop a pharmacological tool for every human protein. These tools are important reagents for scientists studying human health and disease and will facilitate the development of new medicines. It is therefore not surprising that pharmaceutical companies are joining Target 2035, contributing both knowledge and reagents to study novel proteins.

CACHE (Critical Assessment of Computational Hit-finding Experiments): A public-private partnership benchmarking initiative to enable the development of computational methods for hit-finding.

One aspirational goal of computational chemistry is to predict potent and drug-like binders for any protein, such that only those that bind are synthesized. In this Roadmap, we describe the launch of Critical Assessment of Computational Hit-finding Experiments (CACHE), a public benchmarking project to compare and improve small molecule hit-finding algorithms through cycles of prediction and experimental testing. Participants will predict small molecule binders for new and biologically relevant protein targets representing different prediction scenarios.

Identification of Highly Selective Orexin 1 Receptor Antagonists Driven by Structure-Based Design.

OX1 receptor antagonists are of interest to treat, for example, substance abuse disorders, personality disorders, eating disorders, or anxiety-related disorders. However, known dual OX1/OX2 receptor antagonists are not suitable due to their sleep-inducing effects; therefore, we were interested in identifying a highly OX1 selective antagonist with a sufficient window to OX2-mediated effects. Herein, we describe the design of highly selective OX1 receptor antagonists driven by the X-ray structure of OX1 with suvorexant, a dual OX1/OX2 receptor antagonist.

Comparison of Large Chemical Spaces.

Chemical libraries are commonplace in computer-aided drug discovery, and assessing their overlap/complementarity is a routine task. For this purpose, different techniques are applied, ranging from exact matching to comparing physicochemical properties. However, these techniques are applicable only if the compound sets are not too big.

The power of combining phenotypic and target-focused drug discovery.

A fierce dispute has arisen between the supporters of phenotypic and target-focused screening regarding which path grants the higher probability of successful drug development. A chance to reconcile these two approaches lies in successful target deconvolution (TD) after phenotypic screens. But, despite the panoply of available in vitro TD methods, the task of matching a phenotypically active compound with a biomolecular target remains challenging.

Peptidic Macrocycles - Conformational Sampling and Thermodynamic Characterization.

Macrocycles are of considerable interest as highly specific drug candidates, yet they challenge standard conformer generators with their large number of rotatable bonds and conformational restrictions. Here, we present a molecular dynamics-based routine that bypasses current limitations in conformational sampling and extensively profiles the free energy landscape of peptidic macrocycles in solution. We perform accelerated molecular dynamics simulations to capture a diverse conformational ensemble.

Identification of new potent GPR119 agonists by combining virtual screening and combinatorial chemistry.

Virtual screening in a huge collection of virtual combinatorial libraries has led to the identification of two new structural classes of GPR119 agonists with submicromolar in vitro potencies. Herein, we describe the virtual screening process involving feature trees fragment space searches followed by a 3D postprocessing step. The in silico findings were then filtered and prioritized, and finally, combinatorial libraries of target molecules were synthesized.

Design of combinatorial libraries for the exploration of virtual hits from fragment space searches with LoFT.

A case study is presented illustrating the design of a focused CDK2 library. The scaffold of the library was detected by a feature trees search in a fragment space based on reactions from combinatorial chemistry. For the design the software LoFT (Library optimizer using Feature Trees) was used.

Improving similarity-driven library design: customized matching and regioselective feature trees.

Reduced graph descriptors, like feature trees, are frequently applied in cases where the relative arrangement of functional groups is more important than exact substructure matches. Due to their ability to deal with fragmented molecules, they are well-suited for fragment space search and library design. We recently presented LoFT, a novel focused library design approach based on feature trees.

LoFT: similarity-driven multiobjective focused library design.

We present LoFT, a tool for focused combinatorial library design. LoFT provides a set of algorithms, constructing a focused library from a chemical fragment space under optimization of multiple design criteria. A weighted multiobjective scoring function based on physicochemical descriptors is employed for traversing the chemical search space.

Searching Fragment Spaces with feature trees.

Virtual combinatorial chemistry easily produces billions of compounds, for which conventional virtual screening cannot be performed even with the fastest methods available. An efficient solution for such a scenario is the generation of Fragment Spaces, which encode huge numbers of virtual compounds by their fragments/reagents and rules of how to combine them. Similarity-based searches can be performed in such spaces without ever fully enumerating all virtual products.