Decoding Drug Discovery: Exploring A-to-Z In silico Methods for Beginners

Arif Kivrak; Bakhtyar K. Aziz; Bryar A. Hassan; Dlzar D. Ghafour; Hezha O. Rasul; Tarik A. Rashid

REVIEW 5 major objections 6 minor 300 references

Decoding Drug Discovery: Exploring A-to-Z In silico Methods for Beginners

T0 review · 5 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read This paper argues that in silico drug discovery is best understood as one continuous pipeline, from identifying a disease-linked gene to ranking compounds by binding free energy, and it maps every stage for a beginner.

desk verdict A useful beginner map of the CADD pipeline whose teaching value is undercut by three concrete factual errors and an overclaimed conclusion; fixable, and worth refereeing on condition. read the letter →

arxiv 2412.11137 v1 pith:T3ET3ICS submitted 2024-12-15 q-bio.QM cs.AI

Hezha O. Rasul , Dlzar D. Ghafour , Bakhtyar K. Aziz , Bryar A. Hassan , Tarik A. Rashid , Arif Kivrak This is my paper · ORCID

classification q-bio.QMcs.AI

keywords computer-aideddrugdesignmoleculardockingartificialintelligencedynamicsMM-GBSAtargetidentificationvirtualscreeningADMETprediction

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to give a beginner a single readable route through computational drug discovery, from identifying a disease-related gene or protein to ranking candidate compounds by binding free energy. Its central claim is that in silico methods now span this entire A-to-Z process and that learning the pipeline helps researchers cut the time, cost, and attrition of experimental drug development. A sympathetic reader would take away a structured map of the field: omics-based target discovery, hit finding by repurposing, high-throughput and virtual screening, lead optimization, molecular docking, drug-likeness and ADMET filters, molecular dynamics, and MM-PBSA/MM-GBSA rescoring. The contribution is educational synthesis rather than a new experimental result, and its usefulness stands or falls on whether the described methods are represented faithfully.

What carries the argument

The central object is the A-to-Z in silico drug discovery pipeline itself: a staged workflow that connects a disease-associated target to a ranked set of candidate compounds. Its load-bearing role is organizational, since each stage supplies a named computational mechanism, such as the Rule of Five physicochemical cutoffs for drug-likeness, docking scoring functions for pose ranking, ADMET prediction models for pharmacokinetic filtering, and the MM-PB(GB)SA free-energy formulas for rescoring. The pipeline carries the argument by showing that these otherwise separate techniques are steps in one sequence, so that the output of one stage is the input of the next.

What would settle it

A concrete check would be to run the paper's described MM-GBSA single-trajectory protocol on a benchmark set of protein-ligand complexes with measured binding affinities and see whether the computed relative rankings reproduce experiment; if they do not, the review's implicit assertion that this rescoring step improves hit ranking is contradicted.

Watch

Extended reading notes

Core claim

On the authors' own terms, the central claim is that computational methods have matured into a coherent, stage-by-stage pipeline for drug discovery and that this pipeline can be taught as a single narrative. The paper walks from target identification through genomics, proteomics, transcriptomics, metabolomics, and structure prediction; moves to hit discovery via drug repurposing, high-throughput screening, virtual screening, and network pharmacology; then covers hit-to-lead and lead optimization with QSAR, de novo design, and fragment-based design. It closes the pipeline with molecular docking, drug-likeness and ADMET prediction, molecular dynamics simulation, and MM-PB(GB)SA binding free energy calculations, arguing that each stage narrows the chemical space and feeds better candidates into the next. The implicit assertion is that a beginner who follows this sequence can understand how in silico methods accelerate and de-risk the drug development process.

Load-bearing premise

The load-bearing premise is that the cited references and the paper's brief descriptions accurately represent how these in silico methods actually work in practice, and that the selection of topics and references is representative enough to justify calling the result a systematic review.

Editorial extensions

If this is right

A beginner can follow one continuous workflow from a disease-associated gene to a shortlist of candidate compounds, rather than learning each method in isolation.
Applying drug-likeness and ADMET filters early should reduce the number of compounds that fail later because of poor absorption, metabolism, or toxicity.
Ligand- and structure-based virtual screening can shrink libraries of millions of compounds to a small set worth experimental testing, lowering the cost of high-throughput screening.
Rescoring docked poses with MM-PB(GB)SA should improve the ranking of hit compounds and feed more reliable candidates into in vitro validation.
AI and deep learning, including deep-learning protein structure prediction, are presented as making target identification and hit finding faster and cheaper than purely experimental approaches.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

An implication the paper leaves implicit is that the pipeline is modular: replacing any single stage with a newer method, such as a newer machine-learning scoring function, should not disrupt the rest of the workflow.
The paper asserts that AI can reduce drug-development attrition; a natural test that would give this claim quantitative support is a prospective comparison of AI-selected and conventionally selected candidates in early clinical studies.
The pipeline framing suggests a natural next step: turning each described stage into a hands-on tutorial with a small worked example, something the review itself leaves to the reader.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Here's my take: this is a beginner-oriented review, not a research result, and its value is entirely pedagogical. As a map of the CADD pipeline—target ID, docking, virtual screening, ADMET, MD, MM-GBSA—it is mostly coherent and well-organized. The MD simulation setup section and the MM-GBSA equations are the strongest parts. The paper introduces no new methods or data, so its significance is limited to education and orientation. That is a legitimate contribution if the facts are right.

The facts are not all right, and they are wrong in exactly the places a beginner would copy. Section 6.1 calls MDCK an 'apparent permeability coefficient'; MDCK is a cell line. Section 6.5 calls the hERG potassium channel 'a vital antigen'; hERG is an ion channel and off-target. Section 3.3.1 says thalidomide 'selectively inhibits cytosine' in leprosy patients; the relevant mediator in erythema nodosum leprosum is TNF-α. These are not stylistic slips. In a teaching review, they are load-bearing errors.

The Conclusion also says 'the binding free energy was then computed,' but no computation appears anywhere in the paper; Section 8 is a description of methods. Calling the paper a 'systematic review' without any search protocol or inclusion criteria is an overclaim, and the closing block of 18 unrelated self-citations from the same group's optimization and clustering papers does not help. The abstract and body also have typos ('has merged,' 'wing research works'). None of this is fatal in the sense of sinking the whole enterprise, but it does mean the central claim—that this is a reliable beginner overview—is not yet supported.

Credit where it is due: the broad structure is sensible, the standard references are mostly appropriate, and with corrections the paper could serve as a useful entry point for students. The errors are localized and fixable. What I would not do is let it through as is, and I would not cite it in its current form.

Recommendation: this deserves a serious referee, not a desk rejection. Send it out with instructions that the referee should check every technical claim in Sections 3, 6, and 8 and require the self-citation block and the 'systematic review' claim to be addressed. If the authors clean up the factual errors and overclaims, the result is a usable teaching review. If they don't, it isn't.

Referee Report

5 major / 6 minor

Summary. This manuscript is a narrative, beginner-oriented review of in silico methods in drug discovery, covering artificial intelligence and machine learning, target identification (genomics, proteomics, transcriptomics, metabolomics, protein structure prediction), hit identification (drug repurposing, HTS, virtual screening, network pharmacology), hit-to-lead and lead optimization (QSAR, de novo design, FBDD), molecular docking (flexibility types, search methods, scoring functions, QM/MM, DFT), drug-likeness rules, ADMET prediction, molecular dynamics simulation workflows, and binding free energy estimation by MM-PB(GB)SA. The paper's stated goal is to give beginners an A-to-Z account of computational drug discovery with emphasis on target identification at the genetic or protein level, and it claims to have both performed a "systematic review" and "computed" a binding free energy in the course of the work.

Significance. If the technical descriptions were reliable, this would be a useful entry point for students entering computational drug discovery: the coverage is broad and mostly current, including AlphaFold, QM/MM docking, DFT applications, network pharmacology, and standard MD simulation practice. The paper ships no new derivations or quantitative predictions, so its value rests entirely on the accuracy and clarity of its pedagogical descriptions. The strengths are genuine: Section 7.1 gives a correct and usable step-by-step MD setup checklist, Section 8 presents the standard MM-PBSA/MM-GBSA equations faithfully, and the reference list is extensive, including the authors' own prior computational studies. However, the educational claim is currently not supported because several core method descriptions in sections a beginner would rely on contain concrete factual errors, and the self-described "systematic" methodology is not backed by any reproducible search protocol.

major comments (5)

[§6.1 (Absorption)] The sentence "The in vitro gold standard for determining how effectively substances is absorbed into the body is known as the apparent permeability coefficient or MDCK" conflates a cell line with a measured quantity. Madin-Darby canine kidney (MDCK) is a cell line used in permeability assays; the measured quantity is the apparent permeability coefficient (Papp). The same paragraph also lists "Membrane Permeability (Caco2 and MDCK)" as "two representative qualities," which is wording a beginner will misread. This needs to be corrected to distinguish assay systems from the permeability coefficients they produce.
[§6.5 (Toxicity)] The text states that "the hERG K+ channel is a vital antigen to consider early in drug development." hERG is a voltage-gated potassium ion channel and a well-known off-target whose blockade causes QT prolongation and cardiotoxicity; it is not an antigen. Describing it as an antigen teaches an incorrect concept in precisely the section where beginners learn why hERG screening matters.
[§3.3.1 (Drug repurposing)] The statement that "cytosine, found in high amounts in leprosy patients, is selectively inhibited by thalidomide" is factually wrong. The relevant mechanism of thalidomide in erythema nodosum leprosum is inhibition of TNF-α production, not inhibition of the nucleic acid base cytosine. Because this is presented as the reason the FDA approved thalidomide for ENL in 1998, the error directly corrupts the pedagogical example of successful drug repurposing.
[§1 and §9 (Introduction and Conclusion)] Both the Introduction ("the binding free energy is calculated in Section 8") and the Conclusion ("The binding free energy was then computed") claim that a binding free energy calculation was performed in this work. Section 8 only reviews the MM-PB(GB)SA formalism and provides standard equations; no system, no trajectory, and no numerical ΔGbind result appears anywhere in the manuscript. These sentences should be rewritten to say that the methods were reviewed rather than that a calculation was executed.
[§9 (Conclusion)] The Conclusion characterizes the paper as "This systematic review" without providing any search protocol, database list, inclusion/exclusion criteria, or PRISMA-style documentation. As written, the manuscript is a narrative review with a selective reference set. Either add a reproducible methodology section to justify the term "systematic," or re-label the paper as a narrative/educational review.

minor comments (6)

[Abstract and §9] The abstract contains the typo "approaches has merged" (should be "approaches have emerged"), and the Conclusion contains the garbled phrase "the following research works wing research works" before reference [322]; both need copyediting.
[§3.1.6.1 (Known 3D Protein Structures)] The sentence "When 3D structures are accessible through resources like the Protein Data Bank (PDB) and the EMDataBank for cryo-electron microscopy structures" is grammatically incomplete and should be finished or merged with the following sentence.
[§7 (Molecular Dynamics Simulations)] The numbered algorithm for an MD simulation is confusing: after listing step 2 as force calculation and step 3 as updating coordinates/velocities, the text says "In step 2, the updated location and velocity are utilized as inputs, and in step 3, a new time step is generated," which reverses the natural roles of the two steps. Please renumber or rewrite for clarity.
[§3.3.3.2 (Structure-based virtual screening)] The word "draggability" appears in the sentence about considering the target's properties; this should be "druggability."
[§4.1 (Fundamental concepts of molecular docking)] The lock-and-key description is internally confusing: after stating that drug and receptor are viewed as immovable locks and keys, the next sentence says the model can explain modest conformational changes before and after binding, which is a property of the induced-fit picture. Please clarify which model does what.
[References and Author Contributions] Several reference entries contain the homoglyph "hƩps" instead of "https" (e.g., refs [1], [258], and others), and the Author Contributions list a contributor "YAR" who does not appear in the author list (likely a typo for "TAR"). These formatting issues should be fixed in revision.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a narrative review with no derivation or prediction chain to collapse.

full rationale

This manuscript is a beginner-oriented review of in silico drug discovery methods. It contains no quantitative derivation, no fitted parameters, and no prediction that is subsequently compared with data; consequently there is no input-output loop that could reduce to itself by construction. The technical sections (docking, MM-GBSA, MD simulation) restate standard formulas and workflows from the external literature rather than deriving new results from the authors' own premises. The only notable self-referential element is the closing block of recommended readings [322]-[339], which are optional 'future reading' suggestions and are not used to justify any technical claim; a self-citation block that is not load-bearing is a citation-practice concern, not circular reasoning. Concerns raised by reviewers about factual accuracy (e.g., MDCK/Papp terminology in Section 6.1, hERG described as an antigen in Section 6.5, thalidomide/cytosine in Section 3.3.1) and the unsupported 'systematic review' label in the Conclusion are correctness and rigor issues, not circularity. The review's educational value depends on the accuracy of external method descriptions, but that dependency is not circular in the sense of a claimed derivation being equivalent to its inputs.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The paper introduces no new quantitative model, so there are no fitted parameters and no invented entities. The central claims rest on two domain assumptions: that in silico methods work as generally described, and that the cited sources accurately represent the methods presented.

assumptions (2)

domain assumption In silico methods are reliable and efficient for drug target identification and drug candidate optimization.
The review's motivation assumes that computational approaches can expedite drug development. This premise is supported by cited literature but is not tested or argued in this paper.
domain assumption The surveyed software tools, algorithms, and workflows are accurately described by the cited references.
The review's educational value depends on the correctness of its descriptions of methods such as AlphaFold, AutoDock, MM-GBSA, and virtual screening pipelines. No independent validation of these descriptions is provided.

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Cite this review

Pith. "Pith review of Decoding Drug Discovery: Exploring A-to-Z In silico Methods for Beginners." pith.science (2026). https://pith.science/paper/T3ET3ICS

@misc{pith2026241211137,
  author       = {Pith},
  title        = {Pith review of: Decoding Drug Discovery: Exploring A-to-Z In silico Methods for Beginners},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T3ET3ICS}},
  note         = {Machine review of arXiv:2412.11137}
}

read the original abstract

The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional methods such as in vivo and in vitro approaches are limited in their ability to analyze vast amounts of data efficiently, leading to wasteful outcomes. To expedite and streamline drug development, an increasing reliance on computer-aided drug design (CADD) approaches has merged. These sophisticated in silico methods offer a promising avenue for efficiently identifying viable drug candidates, thus providing pharmaceutical firms with significant opportunities to uncover new prospective drug targets. The main goal of this work is to review in silico methods used in the drug development process with a focus on identifying therapeutic targets linked to specific diseases at the genetic or protein level. This article thoroughly discusses A-to-Z in silico techniques, which are essential for identifying the targets of bioactive compounds and their potential therapeutic effects. This review intends to improve drug discovery processes by illuminating the state of these cutting-edge approaches, thereby maximizing the effectiveness and duration of clinical trials for novel drug target investigation.

Figures

Figures reproduced from arXiv: 2412.11137 by the authors.

**Figure 1.** An extensive breakdown of the drug discovery and development process The second main stage, drug development, often begins with identifying a single chemical, which proceeds through numerous studies to support its clearance for sale by the proper regulatory agencies. Preclinical research, clinical trials, and drug registration are three further stages that vary depending on the phase of drug development. As a result… view at source ↗

**Figure 2.** Machine learning techniques in drug discovery and development The artificial neural networks used in deep learning (DL) can learn and adapt from massive experimental data. The advent of big data, along with data mining and algorithm technologies, has the potential to progress the area of personalized medicine based on genetic markers and lead to the discovery or repurposing of current pharmaceuticals that may be mor… view at source ↗

**Figure 3.** Implementation of artificial intelligence in drug discovery 3. Drug Discovery and Drug Design 3.1 Target discovery Small molecules, peptides, antibodies, and emerging modalities like short RNAs and cell therapies are the backbone of drug discovery because of their ability to influence the activity of a biological target and, hence, affect the disease state [27]. Two leading causes of drug failure in clinical trials … view at source ↗

Figures from the paper (11 more)

**Figure 4.** Figure 4: Different approaches in drug target identification Using a computational technique to find, prioritize, and select prospective disease targets is what ‘data mining’ means in this context [30]. However, to produce statistically effective models that can generate predict…

**Figure 5.** Figure 5: Drug repurposing: Drug approved for X(Green), finding new target disease named Y (Yellow) from an old existing drug AI makes the process of repurposing drugs more appealing and practical. Using an already approved treatment for a different disease is favorable since it…

**Figure 6.** Figure 6: Steps involved in the virtual screening process The methods used for virtual screening can be broken down into two distinct groups. The first is the LigandBased Virtual Screening (LBVS) method, which finds promising compounds based on how closely they resemble already…

**Figure 7.** Figure 7: Types of virtual screening: ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS), as well as corresponding computational methods [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]

**Figure 9.** Figure 9: Principles of de novo drug design [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]

**Figure 10.** Figure 10: Molecular docking models 4.2. Molecular docking types 4.2.1. Flexible docking Flexible docking allows the ligand and receptor conformations to be flexible during the docking calculation. This docking simulation is widely used to rigorously analyze the identification o…

**Figure 11.** Figure 11: Criteria of Lipinski's Rule of Five Analysis of successful and unsuccessful clinical candidates has further helped solidify the RoF's foundational principles. However, there is now more evidence to suggest that high lipophilicity is the primary physicochemical charact…

**Figure 12.** Figure 12: Natural origin drugs that violate the Lipinski rule of five Predicting these features is crucial because compounds with problems relating to the pharmacokinetic parameters frequently need additional research before being approved by the national regulatory authority. …

**Figure 13.** Figure 13: The drug must first be sufficiently absorbed [PITH_FULL_IMAGE:figures/full_fig_p031_13.png]

**Figure 13.** Figure 13: Explanation of ADMET characteristics 6.1. Absorption Potential drugs face their first challenge in absorption since they must enter the bloodstream before being active within the body [262]. Drug absorption is intricately connected to numerous features: Membrane Perme…

**Figure 14.** Figure 14: Basic steps involved in the MD simulation process 8. Binding Free Energy Calculation Molecular dynamics-based binding free energy estimates can improve the accuracy of ranking the hit compounds, significantly affecting the hit identification phase. Many essential biol…

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

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