[Type here] The whitepaper provides an overview of the process, types, software’s used, success and challenges of Computer-Aided Drug Designing (CADD) Authors: Jagmohan Verma, Anjaly Maria VRB Analytics Pvt Ltd 21st April 2020 Version 1.0 21st April, 2020 Computer Aided Drug Design “A binding pocket for a new class of drugs to treat AIDS was discovered using docking while considering the flexibility of the receptor through molecular dynamics. This information leads to discovery of orally available HIV integrase inhibitor, raltegravir (Isentress®), approved by FDA in 2007 and received approval for paediatric use in 2011”. This is one of the many success stories of CADD approach. What is Computer-Aided Drug Designing? The traditional drug designing process takes almost 10 years and costs more than 1 billion dollars in total. Several technologies were used to reduce the time and cost of discovering a new drug molecule, one of which was Computer Assisted Drug Designing (CADD). 10,000 250 5 compounds 1 drug Compounds compounds Drug Discovery Preclinical Phase Clinical Phase 1-4 FDA Approved 10-14 years >1 billion dollars Figure 1: Traditional Drug Discovery Process In simple terms, computational drug designing can be explained as a modern drug discovery technique that uses theoretical and computational approaches to design a new drug molecule. CADD approaches can reduce the cost of drug discovery and development up to 50%. Create new Drug molecule candidate Dock Estimate drug molecule to like property target protein Estimate Analyze binding molecular strength interactions Figure 2: Basic Principle of CADD Page 2 of 16 21st April, 2020 Computer Aided Drug Design Major Types of Approaches in CADD CADD approaches are mainly of two types. • Structure based drug design / direct approach • Ligand based drug design / indirect approach Figure 3: Schematic Overview of CADD Process What is structure-based approach? Structure based approach or direct approach is exactly what the term indicates. It depends on the 3D structure of the molecule. The structure of the target protein is known. The basic principle behind structure-based approach involves predicting whether the given small molecule will bind to a chosen protein target and, if so, what will be the strength of this molecular recognition. Page 3 of 16 21st April, 2020 Computer Aided Drug Design The first step in this process is molecular docking which is the cornerstone of structure-based drug design. To accurately carry out docking studies one requires the high-resolution X-ray, NMR or homology-modelled structure with known/predicted binding site in the biomolecule. Molecular docking is done to predict the most probable geometry and position of a small molecule at the surface of a protein by optimizing the interactions between both molecular partners. Many docking programs are freely available and can be used for educational purposes, including web-based tools such as SwissDock.ch, or downloadable programs such as Autodock and Autodock Vina. Almost 162,529 structures are available in the Protein Data Base till date. The next step is to determine the strength of binding of the small molecule to the protein which can be achieved using a binding free energy estimator. Several computer-aided approaches are available for this purpose. They are generally based on high-level methods involving concepts in physical chemistry and statistical physics. Docking software is used for estimating the binding free energy. The docking process involves two interrelated steps, first step is sampling conformations of the ligand in the active site of the protein: then ranking these conformations via a scoring function Sampling algorithm: sampling algorithms should be able to reproduce the experimental binding mode. There are a huge number of possible binding modes between two molecules based on degrees of freedom of both the ligand and protein. To generate all possible conformations computationally will be very expensive. Thus, various sampling algorithms have been developed and widely used in molecular docking software. Sampling algorithms are classified based on the number of degrees of freedom they ignore. Molecular dynamics (MD) is widely used as a powerful simulation method in many fields of molecular modelling. In the context of docking, by moving each atom separately in the field of the rest atoms, MD simulation represents the flexibility of both the ligand and protein more effectively than other algorithms. Page 4 of 16 21st April, 2020 Computer Aided Drug Design The simplest of the algorithms introduced treated the molecules as two rigid bodies thereby reducing the degree of freedom to just six. Examples: DOCK, LibDock, LIDAEUS, SANDOCK Incremental construction: ligand is fragmented from rotatable bonds into various segments. One of the segments is anchored to the receptor surface. The anchor is generally considered to be the fragment which shows maximum interactions with the receptor surface, has minimum number of alternate conformations and fairly rigid such as the ring system. Examples: DOCK4.0, FlexX, SLIDE Monte Carlo (MC):a ligand is modified gradually using bond rotation and translation or rotation of the entire ligand. More than one parameter can also be changed at a time to get a particular conformation. That conformation is then evaluated at the binding site based on energy calculation using molecular mechanics and is then rejected or accepted for the next iteration based on Boltzmann’s probability constant. Example: DockVision 1.0.3, FDS, GlamDock, ICM, MCDOCK Genetic algorithm (GA): It is quite similar to MC method and is basically used to find the global minima. These are much inspired by the Darwin’s Theory of Evolution. GA maintains a population of ligands with an associated fitness determined by the scoring function. Each ligand represents a potential hit. The GA alters the ligands of the population by mutation or crossover. Example: Autodock 4.0, DARWIN, DIVALI , FITTED, FLIPDock Hierarchial algorithm: the low energy conformations of the ligand are pre-computed and aligned. The populations of the pre-generated ligand conformations are merged into a hierarchy such that similar conformations are positioned adjacent to each other within the hierarchy. Afterwards, on carrying out rotation or translation of the ligand, the docking program will make use of this hierarchical data structure and thus minimize the outcomes. Example: GLIDE Figure 4:Types of Algorithm Page 5 of 16 21st April, 2020 Computer Aided Drug Design • Scoring function: It is done to precisely identify the correct poses from incorrect poses, or binders from inactive compounds in a reasonable computation time. However, scoring functions involve estimating, rather than calculating the binding affinity between the protein and ligand. • Assess binding energy by calculating sum of the non-bonded (electrostatistics and van der waals) Classical force field based interactions. Eg DOCK, AutoDock scoring function • The binding energy decomposes in to several energy components, such as hydrogen bond, ionic interactions, hydrophobic effect and bonding Emperical score function entropy. Each component is multiplied by a coefficient and then summed up to give final score. Eg. LUDI, ChemScore • In this method the score is calculated by favouring preferred contacts and penalizing repulsive Knowledge based scoring interactions between easch atom in the ligand and function protein within a given cutoff. Eg.DrugScore, Bleep • It combines several different scores to assess docking conformations. Eg. CScore combines Consensus scoring function DOCK, ChemScore, PMF, GOLD and FlexX scoring functions • MM-PB/SA and MM-GB/SA is involved in rescoring or lead optimization to improve the Physics based scoring accuracy of binding affinity prediction function Figure 5: Types of Scoring Methods Ligand Based Approach: Ligand based drug design is an approach used in the absence of the receptor 3D information and it relies on knowledge of molecules that bind to the Page 6 of 16 21st April, 2020 Computer Aided Drug Design biological target of interest. 3D quantitative structure activity relationships (3D QSAR) and pharmacophore modelling are the most important and widely used tools in ligand-based drug design. Pharmacophore models are derived from known molecules to define the necessary structural characteristics to enable binding to the biological target. The power of prediction is one of the major characteristics of a QSAR model and may be defined as the capability of a model to accurately predict the biological activity of compounds that were not used for model development. Virtual screening methods: Virtual screening (VS) is a computational approach for the discovery of new drugs that has successfully complemented High Throughput Screening (HTS) for hit detection. The objective is to use a computational approach for rapid cost-effective evaluation of large virtual databases of chemical compounds to find novel leads that can be synthesized and examined experimentally for their biological activity. Structure-based virtual screening (SBVS) encompasses a variety of sequential computational phases, including target and database preparation, docking and post docking analysis and prioritization of compounds for biological testing. SBVS is employed in situations in which the 3D structure of the target protein is known. Programs that utilize the SBVS include GLIDE, FlexX and GOLD. Pharmacophore based virtual screening (PBVS) uses a pharmacophore modelling