In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery

Abstract

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds.

Keywords: de novo design; drug discovery; fragment-based; hot spot analysis; in silico methods; lead discovery; machine learning; optimization.

Figure 1

Multiple routes to expanding fragment to more drug-like molecule with improved binding affinity. (A) Traditional medicinal chemistry route: knowledge-based design and synthesis. (B) “Small steps” route: successive cycles of extension of the fragment hit by 1–3 heavy atoms through vectors defined by high-resolution structural characterization methods, such as X-ray crystallography. (C) “Large leaps” or “SAR by catalog” route: from fragment to rule of 5 compliant molecules using virtual screening of commercial compound libraries. (D) Fragment merging route: bridging two overlapping fragments bound at neighbor sites. Regardless of the route, expanded fragments should be checked for biological activity using in vitro, ex vivo, or in vivo assays.

Figure 2

Discovery and structural-optimization of drug-like molecules (A) and fragments (B) using protein target information. The surface represents the binding site. The red and gray colors represent the level of complementarity of ligand with the active site. Pockets with low complementarity with ligand are colored in red; pockets with high complementarity with ligand are highlighted in gray.

Figure 3

Fragment optimization approaches: fragment growing (A), fragment linking (B), and fragment merging (C). The surface of the binding site is depicted in gray. The red and gray colors represent the level of complementarity of ligand with the active site. Pockets with low complementarity with ligand are colored in red; pockets with high complementarity with ligand are highlighted in gray.

Figure 4

Hit to lead progression of an initial fragment (1) to a compound (2) with improved affinity.

Figure 5

Example of a hot spot analysis using FTMap web server of the oncogenic B-RAF kinase, the target of the first marketed drug from fragment-based drug design, vemurafenib. The surface of the binding site is depicted in gray. (A) (PDB ID: 2UVX) the fragment hit (carbon atoms in purple sticks) and the predicted hot spots (yellow dots and surface). (B–D) The iterative growing process of vemurafenib (PDB ID: 3OG7) overlapping the predicted hot spots (the carbon atoms of the fragment hit portion is shown in purple sticks and carbon atoms of the grown portions in yellow sticks).

Figure 6

Cascade virtual screening filtering optimized compounds with the desired activities and properties.

Figure 7

Representation of the integrative approach of generative and predictive deep learning models and transfer learning for fragment-to-lead optimization.

Figure 8

Fragment optimization predecessors and products (Coutard et al., ; Benmansour et al., 2017).